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Renton M, Poot P. Simulation of the evolution of root water foraging strategies in dry and shallow soils. ANNALS OF BOTANY 2014; 114:763-78. [PMID: 24651371 PMCID: PMC4156118 DOI: 10.1093/aob/mcu018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 01/21/2014] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND AIMS The dynamic structural development of plants can be seen as a strategy for exploiting the limited resources available within their environment, and we would expect that evolution would lead to efficient strategies that reduce costs while maximizing resource acquisition. In particular, perennial species endemic to habitats with shallow soils in seasonally dry environments have been shown to have a specialized root system morphology that may enhance access to water resources in the underlying rock. This study aimed to explore these hypotheses by applying evolutionary algorithms to a functional-structural root growth model. METHODS A simulation model of a plant's root system was developed, which represents the dynamics of water uptake and structural growth. The model is simple enough for evolutionary optimization to be computationally feasible, yet flexible enough to allow a range of structural development strategies to be explored. The model was combined with an evolutionary algorithm in order to investigate a case study habitat with a highly heterogeneous distribution of resources, both spatially and temporally--the situation of perennial plants occurring on shallow soils in seasonally dry environments. Evolution was simulated under two contrasting fitness criteria: (1) the ability to find wet cracks in underlying rock, and (2) maximizing above-ground biomass. KEY RESULTS The novel approach successfully resulted in the evolution of more efficient structural development strategies for both fitness criteria. Different rooting strategies evolved when different criteria were applied, and each evolved strategy made ecological sense in terms of the corresponding fitness criterion. Evolution selected for root system morphologies which matched those of real species from corresponding habitats. CONCLUSIONS Specialized root morphology with deeper rather than shallower lateral branching enhances access to water resources in underlying rock. More generally, the approach provides insights into both evolutionary processes and ecological costs and benefits of different plant growth strategies.
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Affiliation(s)
- Michael Renton
- School of Plant Biology and Centre of Excellence for Climate Change Woodland and Forest Health, The University of Western Australia, Crawley, 6009, WA, Australia
| | - Pieter Poot
- School of Plant Biology and Centre of Excellence for Climate Change Woodland and Forest Health, The University of Western Australia, Crawley, 6009, WA, Australia
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Focusing on the golden ball metaheuristic: an extended study on a wider set of problems. ScientificWorldJournal 2014; 2014:563259. [PMID: 25165742 PMCID: PMC4137618 DOI: 10.1155/2014/563259] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 06/06/2014] [Accepted: 06/08/2014] [Indexed: 11/18/2022] Open
Abstract
Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.
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Osaba E, Carballedo R, Diaz F, Onieva E, de la Iglesia I, Perallos A. Crossover versus mutation: a comparative analysis of the evolutionary strategy of genetic algorithms applied to combinatorial optimization problems. ScientificWorldJournal 2014; 2014:154676. [PMID: 25165731 PMCID: PMC4137700 DOI: 10.1155/2014/154676] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Revised: 07/10/2014] [Accepted: 07/14/2014] [Indexed: 11/24/2022] Open
Abstract
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.
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Affiliation(s)
- E. Osaba
- Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, Spain
| | - R. Carballedo
- Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, Spain
| | - F. Diaz
- Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, Spain
| | - E. Onieva
- Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, Spain
| | - I. de la Iglesia
- Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, Spain
| | - A. Perallos
- Deusto Institute of Technology (DeustoTech), University of Deusto, Avenue Universidades 24, 48007 Bilbao, Spain
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55
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Gwak J, Sim KM, Jeon M. Novel dynamic diversity controlling EAs for coevolving optimal negotiation strategies. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.02.153] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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56
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Valdez F, Melin P, Castillo O. Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.02.091] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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57
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Wren CD, Xue JZ, Costopoulos A, Burke A. The role of spatial foresight in models of hominin dispersal. J Hum Evol 2014; 69:70-8. [PMID: 24613470 DOI: 10.1016/j.jhevol.2014.02.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 02/07/2014] [Accepted: 02/07/2014] [Indexed: 11/20/2022]
Abstract
Increasingly sophisticated hominin cognition is assumed to play an important role in major dispersal events but it is unclear what that role is. We present an agent-based model showing that there is a close relationship between level of foresight, environmental heterogeneity, and population dispersibility. We explore the dynamics between these three factors and discuss how they may affect the capacity of a hominin population to disperse. Generally, we find that high levels of environmental heterogeneity select for increased foresight and that high levels of foresight tend to reduce dispersibility. This suggests that cognitively complex hominins in heterogeneous environments have low dispersibility relative to cognitively less complex organisms in more homogeneous environments. The model predicts that the environments leading up to major episodes of dispersal, such as the initial hominin dispersal into Eurasia, were likely relatively low in spatial heterogeneity and that the dispersing hominins had relatively low foresight.
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Affiliation(s)
- Colin D Wren
- Department of Anthropology, McGill University, 855 Sherbrooke St. West., Montreal, QC H3A 2T7, Canada.
| | - Julian Z Xue
- Department of Biology, McGill University, Montreal, Canada
| | - Andre Costopoulos
- Department of Anthropology, McGill University, 855 Sherbrooke St. West., Montreal, QC H3A 2T7, Canada
| | - Ariane Burke
- Département d'anthropologie, Université de Montréal, Montréal, Canada
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Maesani A, Fernando PR, Floreano D. Artificial evolution by viability rather than competition. PLoS One 2014; 9:e86831. [PMID: 24489790 PMCID: PMC3906060 DOI: 10.1371/journal.pone.0086831] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 10/18/2013] [Indexed: 11/18/2022] Open
Abstract
Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.
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Affiliation(s)
- Andrea Maesani
- Laboratory of Intelligent Systems (LIS), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pradeep Ruben Fernando
- Laboratory of Intelligent Systems (LIS), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dario Floreano
- Laboratory of Intelligent Systems (LIS), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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60
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Lichocki P, Floreano D, Keller L. Selection methods regulate evolution of cooperation in digital evolution. J R Soc Interface 2014; 11:20130743. [PMID: 24152811 DOI: 10.1098/rsif.2013.0743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations' average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.
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Affiliation(s)
- Pawel Lichocki
- Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne, , Station 11, 1015 Lausanne, Switzerland
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61
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Liu B, Ji C. Bayesian Nonparametric Modeling for Rapid Design of Metamaterial Microstructures. INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION 2014; 2014:1-9. [DOI: 10.1155/2014/165102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
We consider the problem of rapid design of massive metamaterial (MTM) microstructures from a statistical point of view. A Bayesian nonparametric model, namely, Gaussian Process (GP) mixture, is developed to generate the mapping relationship from the microstructure’s geometric dimension to the electromagnetic response, which is approximately expressed in a closed form of Drude-Lorentz type model. This GP mixture model is neatly able to tackle nonstationarity, discontinuities in the mapping function. The inference is performed using a Markov chain relying on Gibbs sampling. Experimental results demonstrate that the proposed approach is highly efficient in facilitating rapid design of MTM with accuracy.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Chunlin Ji
- Shenzhen Kuang-Chi Institute of Advanced Technology, Shenzhen 518057, China
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62
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Rahimi H, Nazemizadeh M. Dynamic analysis and intelligent control techniques for flexible manipulators: a review. Adv Robot 2013. [DOI: 10.1080/01691864.2013.839079] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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63
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Renton M. Aristotle and adding an evolutionary perspective to models of plant architecture in changing environments. FRONTIERS IN PLANT SCIENCE 2013; 4:284. [PMID: 23914196 PMCID: PMC3728479 DOI: 10.3389/fpls.2013.00284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 07/10/2013] [Indexed: 06/02/2023]
Affiliation(s)
- Michael Renton
- School of Plant Biology, The University of Western AustraliaPerth, WA, Australia
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64
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Zi B, Ding H, Cao J, Zhu Z, Kecskeméthy A. Integrated Mechanism Design and Control for Completely Restrained Hybrid-Driven Based Cable Parallel Manipulators. J INTELL ROBOT SYST 2013. [DOI: 10.1007/s10846-013-9848-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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65
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Hourdakis E, Trahanias P. Use of the separation property to derive Liquid State Machines with enhanced classification performance. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.07.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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66
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Martínez-Soto R, Castillo O, Aguilar LT, Rodriguez A. A hybrid optimization method with PSO and GA to automatically design Type-1 and Type-2 fuzzy logic controllers. INT J MACH LEARN CYB 2013. [DOI: 10.1007/s13042-013-0170-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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67
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XU Y, WANG Z, ZHU Q. An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints. Chin J Chem Eng 2013. [DOI: 10.1016/s1004-9541(13)60490-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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68
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Köker R. A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Inf Sci (N Y) 2013. [DOI: 10.1016/j.ins.2012.07.051] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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69
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Bio-inspired Optimization Methods on Graphic Processing Unit for Minimization of Complex Mathematical Functions. RECENT ADVANCES ON HYBRID INTELLIGENT SYSTEMS 2013. [DOI: 10.1007/978-3-642-33021-6_25] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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70
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Mabu S, Ye F, Hirasawa K. An Explicit Memory Scheme of Genetic Network Programming. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2012. [DOI: 10.20965/jaciii.2012.p0851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have made significant contribution to the study of evolutionary computation. And recently, a new approach named Genetic Network Programming (GNP) has been proposed especially for solving complex problems in dynamic environments. It is based on the algorithms of classical evolutionary computation techniques and uses data structures of directed graphs which are the unique feature of GNP. Focusing on GNP’s distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for standard GNP in order to improve the performance of GNP by adopting an explicit memory scheme which records and utilizes the exploited information flexibly and extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated in the memory during evolution. Among the accumulated information, some of them are selected and used to guide the agents. In this paper, the proposed architecture is applied to the tileworld which is an excellent benchmark for evaluating the architecture demonstrating its superiority.
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71
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Osoba O, Kosko B. Noise-enhanced clustering and competitive learning algorithms. Neural Netw 2012; 37:132-40. [PMID: 23137615 DOI: 10.1016/j.neunet.2012.09.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2012] [Revised: 09/20/2012] [Accepted: 09/20/2012] [Indexed: 11/30/2022]
Abstract
Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.
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Affiliation(s)
- Osonde Osoba
- Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA
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72
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Abstract
In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a classical gradient descent algorithm used in clustering, viz. K-means algorithm. Hence, the name genetic K-means algorithm (GKA). We define K-means operator, one-step of K-means algorithm, and use it in GKA as a search operator instead of crossover. We also define a biased mutation operator specific to clustering called distance-based-mutation. Using finite Markov chain theory, we prove that the GKA converges to the global optimum. It is observed in the simulations that GKA converges to the best known optimum corresponding to the given data in concurrence with the convergence result. It is also observed that GKA searches faster than some of the other evolutionary algorithms used for clustering.
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Affiliation(s)
- K Krishna
- Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore
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73
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Gwak J, Sim KM. An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies. APPL INTELL 2012. [DOI: 10.1007/s10489-012-0384-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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74
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A hybrid evolutionary programming approach for optimal worst case tolerance design of magnetic devices. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.03.048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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75
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HONG YOUNGDAE, KIM JONGHWAN. AN EVOLUTIONARY OPTIMIZED FOOTSTEP PLANNER FOR THE NAVIGATION OF HUMANOID ROBOTS. INT J HUM ROBOT 2012. [DOI: 10.1142/s0219843612500053] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, an evolutionary optimized footstep planner for the navigation of humanoid robots is proposed. A footstep planner based on a univector field navigation method is proposed to generate a command state (CS) as an input to a modifiable walking pattern generator (MWPG) at each footstep. The MWPG generates associated trajectories of every leg joint to follow the given CS. In order to satisfy various objectives in the navigation, the univector fields are optimized by evolutionary programming. The three objectives, shortest elapsed time to get to a destination, safety without obstacle collision, and less energy consumption, are considered with mechanical constraints of a real humanoid robot, that is, the maximum step length and allowable yawing range of the feet. The effectiveness of the proposed algorithm is demonstrated through both computer simulation and experiment for a small-sized humanoid robot, HanSaRam-IX.
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Affiliation(s)
- YOUNG-DAE HONG
- Department of Electrical Engineering, KAIST, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea
| | - JONG-HWAN KIM
- Department of Electrical Engineering, KAIST, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea
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76
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Xiong J, Yang KW, Liu J, Zhao QS, Chen YW. A two-stage preference-based evolutionary multi-objective approach for capability planning problems. Knowl Based Syst 2012. [DOI: 10.1016/j.knosys.2012.02.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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77
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Thrishantha Nanayakkara DP, Watanabe K, Kiguchi K, Izumi K. Fuzzy self-adaptive radial basis function neural network-based control of a seven-link redundant industrial manipulator. Adv Robot 2012. [DOI: 10.1163/156855301750095569] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- D. P. Thrishantha Nanayakkara
- a Faculty of Engineering Systems and Technology, Graduate School of Science and Engineering, Saga University, 1-Honjomachi, Saga 840-8502, Japan
| | - Keigo Watanabe
- b Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1-Honjomachi, Saga 840-8502, Japan
| | - Kazuo Kiguchi
- c Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1-Honjomachi, Saga 840-8502, Japan
| | - Kiyotaka Izumi
- d Department of Mechanical Engineering, Faculty of Science and Engineering, Saga University, 1-Honjomachi, Saga 840-8502, Japan
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78
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Application of genetic algorithms to determine heavy metal ions sorption dynamics on clinoptilolite bed. CHEMICAL AND PROCESS ENGINEERING-INZYNIERIA CHEMICZNA I PROCESOWA 2012. [DOI: 10.2478/v10176-012-0010-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Application of genetic algorithms to determine heavy metal ions sorption dynamics on clinoptilolite bed
In the last decade a growing interest was observed in low-cost adsorbents for heavy metal ions. Clinoptilolite is a mineral sorbent extracted in Poland that is used to remove heavy metal ions from diluted solutions. The experiments in this study were carried out in a laboratory column for multicomponent water solutions of heavy metal ions, i.e. Cu(II), Zn(II) and Ni(II). A mathematical model to calculate the metals' concentration of water solution at the column outlet and the concentration of adsorbed substances in the adsorbent was proposed. It enables determination of breakthrough curves for different process conditions and column dimensions. The model of process dynamics in the column took into account the specificity of sorption described by the Elovich equation (for chemical sorption and ion exchange). Identification of the column dynamics consisted in finding model coefficients β, KE and Deff and comparing the calculated values with experimental data. Searching for coefficients which identify the column operation can involve the use of optimisation methods to find the area of feasible solutions in order to obtain a global extremum. For that purpose our own procedure of genetic algorithm is applied in the study.
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79
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Kumar R, Jyotishree. Blending Roulette Wheel Selection & Rank Selection in Genetic Algorithms. ACTA ACUST UNITED AC 2012. [DOI: 10.7763/ijmlc.2012.v2.146] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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80
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Modeling of biological intelligence for SCM system optimization. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2011; 2012:769702. [PMID: 22162724 PMCID: PMC3227372 DOI: 10.1155/2012/769702] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2011] [Accepted: 10/15/2011] [Indexed: 11/17/2022]
Abstract
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
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81
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SUN YUNGNIEN, HUANG SHUCHIEN. GENETIC ALGORITHMS FOR ERROR-BOUNDED POLYGONAL APPROXIMATION. INT J PATTERN RECOGN 2011. [DOI: 10.1142/s0218001400000209] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu–Seneviratne algorithm,24 the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.
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Affiliation(s)
| | - SHU-CHIEN HUANG
- Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Taiwan
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82
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GALLAGHER JOHNC. AN EVOLVABLE HARDWARE LAYER FOR GLOBAL AND LOCAL LEARNING OF MOTOR CONTROL IN A HEXAPOD ROBOT. INT J ARTIF INTELL T 2011. [DOI: 10.1142/s021821300500251x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The use of Genetic Algorithms (GAs) to evolve Continuous Time Recurrent Neural Networks (CTRNNs) for locomotion control of a hexapod robot was demonstrated in previous work. Those controllers were shown to be robust to loss of sensory information and other peripheral variations and their operation was explained through qualitative dynamical systems analysis. Although real robot controllers were fielded, our prior methods contained drawbacks limiting the attractiveness of their implementation as real devices. These included centralization of the evolutionary learning algorithms and the inability of those algorithms to evolve effective controllers without inclusion of a priori architectural knowledge. In this paper, we will introduce a generic neuromorphic Evolvable Hardware (EH) learning architecture and show how it can be used to overcome these previous difficulties. We will also introduce a novel modification to previously reported EH methods that improves search efficacy. We will discuss our previous work, introduce relevant concepts from the emerging field of evolvable hardware as well as our chip, and show how it can be used to deliver effective and practical locomotion control. We will focus especially on the integrated systems abilities to self-heal and reconfigure while in service and will examine centralized, decentralized, and hybrid learning configurations.
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Affiliation(s)
- JOHN C. GALLAGHER
- Department of Computer Science and Engineering, Wright State University, Dayton, Ohio 45435-0001, USA
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83
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ISWANDY KUNCUP, KOENIG ANDREAS. FULLY EVOLVED KERNEL METHOD EMPLOYING SVM ASSESSMENT FOR FEATURE COMPUTATION FROM MULTISENSOR SIGNALS. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2011. [DOI: 10.1142/s1469026809002424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The design of intelligent sensor systems for pattern recognition applications requires sophisticated methods from conventional signal processing and computational intelligence. A significant part of the overall system architecture still has to be manually elaborated in a tedious and time consuming process by an experienced designer for each new application or modification. Clearly, an automated method for auto-configuration of sensor systems would be attractive. This paper expands our previous works on automatic design of multisensor systems applying optimized Gaussian kernel for feature computation. The purpose of optimizing feature computation in the design of sensor systems is to increase the classification accuracy and the computation efficiency as well as to reduce storage and computational requirements in embedded sensor systems. Here, we investigate the feature computation of Gaussian kernel assessed by support vector machine (SVM) using genetic algorithms (GA). The experiment results are compared with the previous work using k-nearest neighbor (k-NN) and LDA/k-NN. Our experiments are tested with gas-sensor benchmark data. From the experimental results, we verify that SVM can achieve better results than k-NN and LDA/k-NN methods.
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Affiliation(s)
- KUNCUP ISWANDY
- Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-Strasse, Kaiserslautern, Rheinland Pfalz D-67663, Germany
| | - ANDREAS KOENIG
- Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-Strasse, Kaiserslautern, Rheinland Pfalz D-67663, Germany
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84
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Tatt Hee Oong, Isa NAM. Adaptive Evolutionary Artificial Neural Networks for Pattern Classification. ACTA ACUST UNITED AC 2011; 22:1823-36. [DOI: 10.1109/tnn.2011.2169426] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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85
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86
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Gomez S, Ivorra B, Ramos AM. Optimization of a pumping ship trajectory to clean oil contamination in the open sea. ACTA ACUST UNITED AC 2011. [DOI: 10.1016/j.mcm.2011.02.037] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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87
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An inverse parameter identification procedure assessing the quality of the estimates using Bayesian neural networks. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2011.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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88
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89
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Abstract
Discontinuous curve fitting is the task of finding a suitable function which is best fitting to a given set of data points. Once the expression forms of function and of error are determined, curve fitting is regarded as an optimization problem. In this paper, a measurement method is proposed to optimize curve fitting problem. The proposed method is based on genetic algorithm, namely GAOCF (GA based Optimizer for Curve Fitting). The main advantage is that the proposed GA based method can skip the local optimum solutions, and find out better solution in a huge searching space. Consequently, some arc data will be used to verify the feasibility and effectiveness of the GA based measurement method proposed in this paper.
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90
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Wan X, Cai S, Zhou J, Liu Z. Emergence of modularity and disassortativity in protein-protein interaction networks. CHAOS (WOODBURY, N.Y.) 2010; 20:045113. [PMID: 21198125 DOI: 10.1063/1.3517107] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks.
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Affiliation(s)
- Xi Wan
- Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200072, China
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91
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Giles CL, Sun R, Zurada JM. Neural networks and hybrid intelligent models: foundations, theory, and applications. ACTA ACUST UNITED AC 2010; 9:721-3. [PMID: 18255761 DOI: 10.1109/tnn.1998.712147] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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92
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Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy. Inf Sci (N Y) 2010. [DOI: 10.1016/j.ins.2009.12.029] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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93
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Hossain MT, Mabu S, Hirasawa K. An Evolutionary Negotiation Model Using Genetic Network Programming. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2010. [DOI: 10.20965/jaciii.2010.p0215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In a common two-party price negotiation, the buyer and the seller try to maximize inherently conflicting objectives regarding the price of a product. Generating an intelligent solution for both the buyer and the seller is often difficult, time-consuming and inefficient under such conditions. In this work, we therefore devise an intelligent solution for such complex situations in Business-to-Business E-business activities. The main contribution of this paper is the design of an intelligent decision support mechanism for an intelligent and automated negotiation solution based on a newly developed evolutionary computing technique called Genetic Network Programming (GNP). In addition, we devise a new Negotiation Protocol aiming to develop a set of rules for agents’ behavior during the negotiation process. Moreover, to reach a negotiation solution, concession that is usually predetermined in the conventional system is needed. To the contrary, in this research, concession is determined automatically by evolution that makes our system intelligent, automated, and efficient. Extensive simulations are emphasized to study the characteristics and behaviors of agents obtained by evolution.
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94
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Huang Q, Li LL, Yang SY. PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility. J Mol Graph Model 2010; 28:775-87. [PMID: 20206562 DOI: 10.1016/j.jmgm.2010.02.002] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2009] [Revised: 01/25/2010] [Accepted: 02/07/2010] [Indexed: 02/05/2023]
Abstract
This account describes a new pharmacophore-based de novo design method of drug-like molecules (PhDD). The method PhDD first generates a set of new molecules that completely conform to the requirements of a given pharmacophore model, followed by a series of assessments to the generated molecules, including assessments of drug-likeness, bioactivity, and synthetic accessibility. PhDD is tested on three typical examples, namely, pharmacophore hypotheses of histone deacetylase (HDAC), cyclin-dependent kinase 2 (CDK2) and HIV-1 integrase (IN) inhibitors. The test results demonstrate that PhDD is able to generate molecules with novel structures but having similar biological functions with existing inhibitors. The validity of PhDD together with its ability of assessing synthetic accessibility makes it a useful tool in rational drug design.
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Affiliation(s)
- Qi Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, #1 Keyuan Road 4, Chengdu, Sichuan 610041, China
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95
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Xiang X, Zhang D, Qin J, Fu Y. Ant Colony with Genetic Algorithm Based on Planar Graph for Multiple Sequence Alignment. ACTA ACUST UNITED AC 2010. [DOI: 10.3923/itj.2010.274.281] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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96
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A New Optimization Method Based on a Paradigm Inspired by Nature. SOFT COMPUTING FOR RECOGNITION BASED ON BIOMETRICS 2010. [DOI: 10.1007/978-3-642-15111-8_17] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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97
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Martinez R, Castillo O, Aguilar LT, Rodriguez A. Optimization of Type-2 Fuzzy Logic Controllers Using PSO Applied to Linear Plants. STUDIES IN COMPUTATIONAL INTELLIGENCE 2010. [DOI: 10.1007/978-3-642-15534-5_11] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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98
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Wang L, Mabu S, Ye F, Eto S, Fan X, Hirasawa K. Genetic Network Programming with Rule Accumulation and its Application to Tile-World Problem. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2009. [DOI: 10.20965/jaciii.2009.p0551] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genetic Network Programming (GNP) is an evolutionary algorithm derived from GA and GP. Directed graph structure, reusability of nodes, and implicit memory function enable GNP to deal with complex problems in dynamic environments efficiently and effectively, as many paper demonstrated. This paper proposed a new method to optimize GNP by extracting and using rules. The basic idea of GNP with Rule Accumulation (GNP with RA) is to extract rules with higher fitness values from the elite individuals and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represent the good experiences of the past behavior. As a result, the rule pool serves as an experience set of GNP obtained in the evolutionary process. By extracting the rules during the evolutionary period and then matching them with the situations of the environment, we could, for example, guide agents' behavior properly and get better performance of the agents. In this paper, we apply GNP with RA to the problem of determining agents' behaviors in the Tile-world environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP both in the average fitness value and its stability.
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99
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Meza J, Yildirim M, Masud A. A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning. ACTA ACUST UNITED AC 2009. [DOI: 10.1109/tsmca.2009.2025868] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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100
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Siang Yew Chong, Tino P, Xin Yao. Relationship Between Generalization and Diversity in Coevolutionary Learning. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2009. [DOI: 10.1109/tciaig.2009.2034269] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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