Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586219
L. Veronese, R. Krohling
Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform. In case of Evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of the Differential Evolution (DE) algorithm in C-CUDA. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced using C-CUDA. As far as we know, this is the first implementation of DE algorithm in C-CUDA.
{"title":"Differential evolution algorithm on the GPU with C-CUDA","authors":"L. Veronese, R. Krohling","doi":"10.1109/CEC.2010.5586219","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586219","url":null,"abstract":"Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform. In case of Evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of the Differential Evolution (DE) algorithm in C-CUDA. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced using C-CUDA. As far as we know, this is the first implementation of DE algorithm in C-CUDA.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"31 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80998395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586309
Kit Yan Chan, T. Dillon, Che Kit Kwong
Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods.
{"title":"Polynomial modeling for manufacturing processes using a backward elimination based genetic programming","authors":"Kit Yan Chan, T. Dillon, Che Kit Kwong","doi":"10.1109/CEC.2010.5586309","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586309","url":null,"abstract":"Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"16 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81968497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586300
M. Tasgetiren, Q. Pan, P. N. Suganthan, A. Chen
Very recently, Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638–2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84–92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion, respectively. Both algorithms generated excellent results, thus improving all the best known solutions reported in the literature so far. However, in this paper, we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately, 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.
{"title":"A discrete artificial bee colony algorithm for the permutation flow shop scheduling problem with total flowtime criterion","authors":"M. Tasgetiren, Q. Pan, P. N. Suganthan, A. Chen","doi":"10.1109/CEC.2010.5586300","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586300","url":null,"abstract":"Very recently, Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638–2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84–92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion, respectively. Both algorithms generated excellent results, thus improving all the best known solutions reported in the literature so far. However, in this paper, we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately, 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"647 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76266062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5585951
J. J. Valdés
Data fusion approaches are nowadays needed and also a challenge in many areas, like sensor systems monitoring complex processes. This paper explores evolutionary computation approaches to sensor fusion based on unsupervised nonlinear transformations between the original sensor space (possibly highly-dimensional) and lower dimensional spaces. Domain-independent implicit and explicit transformations for Visual Data Mining using Differential Evolution and Genetic Programming aiming at preserving the similarity structure of the observed multivariate data are applied and compared with classical deterministic methods. These approaches are illustrated with a real world complex problem: Failure conditions in Auxiliary Power Units in aircrafts. The results indicate that the evolutionary approaches used were useful and effective at reducing dimensionality while preserving the similarity structure of the original data. Moreover the explicit models obtained with Genetic Programming simultaneously covered both feature selection and generation. The evolutionary techniques used compared very well with their classical counterparts, having additional advantages. The transformed spaces also help in visualizing and understanding the properties of the sensor data.
{"title":"Evolutionary computation based nonlinear transformations to low dimensional spaces for sensor data fusion and Visual Data Mining","authors":"J. J. Valdés","doi":"10.1109/CEC.2010.5585951","DOIUrl":"https://doi.org/10.1109/CEC.2010.5585951","url":null,"abstract":"Data fusion approaches are nowadays needed and also a challenge in many areas, like sensor systems monitoring complex processes. This paper explores evolutionary computation approaches to sensor fusion based on unsupervised nonlinear transformations between the original sensor space (possibly highly-dimensional) and lower dimensional spaces. Domain-independent implicit and explicit transformations for Visual Data Mining using Differential Evolution and Genetic Programming aiming at preserving the similarity structure of the observed multivariate data are applied and compared with classical deterministic methods. These approaches are illustrated with a real world complex problem: Failure conditions in Auxiliary Power Units in aircrafts. The results indicate that the evolutionary approaches used were useful and effective at reducing dimensionality while preserving the similarity structure of the original data. Moreover the explicit models obtained with Genetic Programming simultaneously covered both feature selection and generation. The evolutionary techniques used compared very well with their classical counterparts, having additional advantages. The transformed spaces also help in visualizing and understanding the properties of the sensor data.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"12 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78327869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586143
F. A. Zotes, M. Peñas
The aim of this article is to analyse the results obtained when using a genetic algorithm (GA) to optimise the interplanetary trajectory of a spacecraft. The desired trajectory should visit Saturn, after performing a gravitational assistance or fly-by in planet Mars. The GA tunes a set of variables, in order to achieve the mission purpose while satisfying the constraints and minimizing the delta-V of the mission. Due to the complexity of the implemented models and the lack of analytical solutions, an alternative non-traditional algorithm provided by soft-computing techniques such as GA is necessary to achieve an optimum solution. The positions of planets as provided by Jet Propulsion Laboratory have been considered. A variable mutation rate has been implemented that broadens the search area whenever the population becomes uniform. The results are very useful from the point of view of mission analysis and therefore can be used as an initial guess for further optimizations. They can also be the first step for a more refined analysis and time-consuming simulations based on more complex models of orbital perturbations.
{"title":"Delta-V genetic optimisation of a trajectory from Earth to Saturn with fly-by in Mars","authors":"F. A. Zotes, M. Peñas","doi":"10.1109/CEC.2010.5586143","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586143","url":null,"abstract":"The aim of this article is to analyse the results obtained when using a genetic algorithm (GA) to optimise the interplanetary trajectory of a spacecraft. The desired trajectory should visit Saturn, after performing a gravitational assistance or fly-by in planet Mars. The GA tunes a set of variables, in order to achieve the mission purpose while satisfying the constraints and minimizing the delta-V of the mission. Due to the complexity of the implemented models and the lack of analytical solutions, an alternative non-traditional algorithm provided by soft-computing techniques such as GA is necessary to achieve an optimum solution. The positions of planets as provided by Jet Propulsion Laboratory have been considered. A variable mutation rate has been implemented that broadens the search area whenever the population becomes uniform. The results are very useful from the point of view of mission analysis and therefore can be used as an initial guess for further optimizations. They can also be the first step for a more refined analysis and time-consuming simulations based on more complex models of orbital perturbations.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"64 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77808576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586213
W. Pullan
This paper presents the results obtained using an unbiased Population Based Search (PBS) for optimising Morse atomic clusters. PBS is able to repeatedly obtain all putative global minima for Morse clusters in the range 5 ≤ N ≤ 80, N = 147,ρ = 3,6,10, 14, as reported in the Cambridge Cluster Database. In addition, putative global minima have been established for Morse clusters in the range 81 ≤ N ≤ 146,ρ = 14. The PBS algorithm incorporates and extends key techniques that have been developed in other cluster optimisation algorithms over the last decade. Of particular importance are the use of cut and paste operators, structure niching and a new operator, Directed Optimisation, which extends the previous concept of directed mutation. In addition, PBS is able to operate in a parallel mode for optimising larger clusters.
{"title":"Unbiased geometry optimisation of Morse atomic clusters","authors":"W. Pullan","doi":"10.1109/CEC.2010.5586213","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586213","url":null,"abstract":"This paper presents the results obtained using an unbiased Population Based Search (PBS) for optimising Morse atomic clusters. PBS is able to repeatedly obtain all putative global minima for Morse clusters in the range 5 ≤ N ≤ 80, N = 147,ρ = 3,6,10, 14, as reported in the Cambridge Cluster Database. In addition, putative global minima have been established for Morse clusters in the range 81 ≤ N ≤ 146,ρ = 14. The PBS algorithm incorporates and extends key techniques that have been developed in other cluster optimisation algorithms over the last decade. Of particular importance are the use of cut and paste operators, structure niching and a new operator, Directed Optimisation, which extends the previous concept of directed mutation. In addition, PBS is able to operate in a parallel mode for optimising larger clusters.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"362 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77842952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586148
J. Torres-Jiménez, E. Rodriguez-Tello
This paper presents new upper bounds for binary covering arrays of variable strength constructed by using a new Simulated Annealing (SA) algorithm. This algorithm incorporates several distinguished features including an efficient heuristic to generate good quality initial solutions, a compound neighborhood function which combines two carefully designed neighborhoods and a fine-tuned cooling schedule. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing that the proposed SA algorithm is able to outperform them.
{"title":"Simulated Annealing for constructing binary covering arrays of variable strength","authors":"J. Torres-Jiménez, E. Rodriguez-Tello","doi":"10.1109/CEC.2010.5586148","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586148","url":null,"abstract":"This paper presents new upper bounds for binary covering arrays of variable strength constructed by using a new Simulated Annealing (SA) algorithm. This algorithm incorporates several distinguished features including an efficient heuristic to generate good quality initial solutions, a compound neighborhood function which combines two carefully designed neighborhoods and a fine-tuned cooling schedule. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing that the proposed SA algorithm is able to outperform them.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"9 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78100548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5585956
A. Kattan, R. Poli
Here we propose GP-zip3, a system which uses Genetic Programming to find optimal ways to combine standard compression algorithms for the purpose of compressing files and archives. GP-zip3 evolves programs with multiple components. One component analyses statistical features extracted from the raw data to be compressed (seen as a sequence of 8-bit integers) to divide the data into blocks. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is applied to group similar data blocks. Each cluster is then labelled with the optimal compression algorithm for its member blocks. Once a program that achieves good compression is evolved, it can be used on unseen data without the requirement for any further evolution. GP-zip3 is similar to its predecessor, GP-zip2. Both systems outperform a variety of standard compression algorithms and are faster than other evolutionary compression techniques. However, GP-zip2 was still substantially slower than off-the-shelf algorithms. GP-zip3 alleviates this problem by using a novel fitness evaluation strategy. More specifically, GP-zip3 evolves and then uses decision trees to predict the performance of GP individuals without requiring them to be used to compress the training data. As shown in a variety of experiments, this speeds up evolution in GP-zip3 considerably over GP-zip2 while achieving similar compression results, thereby significantly broadening the scope of application of the approach.
{"title":"Evolutionary synthesis of lossless compression algorithms with GP-zip3","authors":"A. Kattan, R. Poli","doi":"10.1109/CEC.2010.5585956","DOIUrl":"https://doi.org/10.1109/CEC.2010.5585956","url":null,"abstract":"Here we propose GP-zip3, a system which uses Genetic Programming to find optimal ways to combine standard compression algorithms for the purpose of compressing files and archives. GP-zip3 evolves programs with multiple components. One component analyses statistical features extracted from the raw data to be compressed (seen as a sequence of 8-bit integers) to divide the data into blocks. These blocks are then projected onto a two-dimensional Euclidean space via two further (evolved) program components. K-means clustering is applied to group similar data blocks. Each cluster is then labelled with the optimal compression algorithm for its member blocks. Once a program that achieves good compression is evolved, it can be used on unseen data without the requirement for any further evolution. GP-zip3 is similar to its predecessor, GP-zip2. Both systems outperform a variety of standard compression algorithms and are faster than other evolutionary compression techniques. However, GP-zip2 was still substantially slower than off-the-shelf algorithms. GP-zip3 alleviates this problem by using a novel fitness evaluation strategy. More specifically, GP-zip3 evolves and then uses decision trees to predict the performance of GP individuals without requiring them to be used to compress the training data. As shown in a variety of experiments, this speeds up evolution in GP-zip3 considerably over GP-zip2 while achieving similar compression results, thereby significantly broadening the scope of application of the approach.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"17 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72863108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586055
Jun He
Linear functions, as a canonical model of unimodal problems, have been widely used in the theoretical study of evolutionary algorithms (EAs). However in most of cases, only the simplest linear function, i.e. One-Max function, is taken in the theoretical study. A question arises naturally: whether can the results for One-Max function be generalized to linear functions? The main contribution of this paper is to generalize a result about the first hitting time of (1 + λ) EA from One-Max function [1] to linear functions. A new proof is proposed based on drift analysis. This work is a direct extension of the previous analysis of (1 + 1) EA for linear functions [2].
{"title":"A note on the first hitting time of (1 + λ) evolutionary algorithm for linear functions with boolean inputs","authors":"Jun He","doi":"10.1109/CEC.2010.5586055","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586055","url":null,"abstract":"Linear functions, as a canonical model of unimodal problems, have been widely used in the theoretical study of evolutionary algorithms (EAs). However in most of cases, only the simplest linear function, i.e. One-Max function, is taken in the theoretical study. A question arises naturally: whether can the results for One-Max function be generalized to linear functions? The main contribution of this paper is to generalize a result about the first hitting time of (1 + λ) EA from One-Max function [1] to linear functions. A new proof is proposed based on drift analysis. This work is a direct extension of the previous analysis of (1 + 1) EA for linear functions [2].","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"18 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81260455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586349
Jian-Ping Li, Xiaodong Li, A. Wood
The species conservation technique is a relatively new approach to finding multiple solutions of a multimodal optimization problem. When adopting such a technique, a species is defined as a group of individuals in a population that have similar characteristics and are dominated by the best individual, called the species seed. Species conservation techniques are used to identify species within a population and to conserve the identified species in the current generation. A ‘species-based evolutionary algorithm’ (SEA) is the combination of a species conservation technique with an evolutionary algorithm, such as genetic algorithms, particle swarm optimization, or differential evolution. These SEAs have been demonstrated to be effective in searching multiple solutions of a multimodal optimization problem. This paper will briefly review its principles and its variants developed to date. These methods had been used to solve engineering optimization problems and found some new solutions.
{"title":"Species based evolutionary algorithms for multimodal optimization: A brief review","authors":"Jian-Ping Li, Xiaodong Li, A. Wood","doi":"10.1109/CEC.2010.5586349","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586349","url":null,"abstract":"The species conservation technique is a relatively new approach to finding multiple solutions of a multimodal optimization problem. When adopting such a technique, a species is defined as a group of individuals in a population that have similar characteristics and are dominated by the best individual, called the species seed. Species conservation techniques are used to identify species within a population and to conserve the identified species in the current generation. A ‘species-based evolutionary algorithm’ (SEA) is the combination of a species conservation technique with an evolutionary algorithm, such as genetic algorithms, particle swarm optimization, or differential evolution. These SEAs have been demonstrated to be effective in searching multiple solutions of a multimodal optimization problem. This paper will briefly review its principles and its variants developed to date. These methods had been used to solve engineering optimization problems and found some new solutions.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"4 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82325534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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