Pub Date : 2003-12-08DOI: 10.1109/CEC.2003.1299403
D. Corne, Joshua D. Knowles
The No-Free-Lunch (NFL) theorems hold for general multiobjective fitness spaces, in the sense that, over a space of problems which is closed under permutation, any two algorithms will produce the same set of multiobjective samples. However, there are salient ways in which NFL does not generally hold in multiobjective optimization. Previously we have shown that a 'free lunch' can arise when comparative metrics (rather than absolute metrics) are used for performance measurement. Here we show that NFL does not generally apply in multiobjective optimization when absolute performance metrics are used. This is because multiobjective optimizers usually combine a generator with an archiver. The generator corresponds to the 'algorithm' in the NFL sense, but the archiver filters the sample generated by the algorithm in a way that undermines the NFL assumptions. Essentially, if two multiobjective approaches have different archivers, their average performance may differ. We prove this, and hence show that we can say, without qualification, that some multiobjective approaches are better than others.
{"title":"Some multiobjective optimizers are better than others","authors":"D. Corne, Joshua D. Knowles","doi":"10.1109/CEC.2003.1299403","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299403","url":null,"abstract":"The No-Free-Lunch (NFL) theorems hold for general multiobjective fitness spaces, in the sense that, over a space of problems which is closed under permutation, any two algorithms will produce the same set of multiobjective samples. However, there are salient ways in which NFL does not generally hold in multiobjective optimization. Previously we have shown that a 'free lunch' can arise when comparative metrics (rather than absolute metrics) are used for performance measurement. Here we show that NFL does not generally apply in multiobjective optimization when absolute performance metrics are used. This is because multiobjective optimizers usually combine a generator with an archiver. The generator corresponds to the 'algorithm' in the NFL sense, but the archiver filters the sample generated by the algorithm in a way that undermines the NFL assumptions. Essentially, if two multiobjective approaches have different archivers, their average performance may differ. We prove this, and hence show that we can say, without qualification, that some multiobjective approaches are better than others.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134278295","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299569
Y. Takahashi, Y. Kawano, M. Kitagawa
We investigate a class QNC/sup 0/ (ADD) that is QNC/sup 0/ with gates for addition of two binary numbers, where QNC/sup 0/ is a class consisting of quantum operations computed by constant-depth quantum circuits. We show that QNC/sup 0/(ADD) = QNC/sup 0/(PAR), where QNC/sup 0/(PAR) is QNC/sup 0/ with Toffoli gates of arbitrary fan-in and gates for parity. Moreover, we show that QNC/sup 0/(ADD) = QAC/sup 0/(MUL) = QAC/sup 0/(DIV), where QAC/sup 0/(MUL) and QAC/sup 0/(DIV) are QNC/sup 0/ with Toffoli gates of arbitrary fan-in and gates for multiplication and division respectively. In the classical setting, similar relationships do not hold. These relationships suggest that QNC/sup 0/ /spl subne/ QNC/sup 0/(ADD); that is, the use of gates for addition increases the computational power of constant-depth quantum circuits. To prove QNC/sup 0/ /spl subne/ QNC/sup 0/(ADD), we present a characterization of this relationship by the one-wayness of a permutation that is constructed explicitly. We conjecture that the permutation is one-way, which implies QNC/sup 0/ /spl subne/ QNC/sup 0/(ADD).
{"title":"On the computational power of constant-depth quantum circuits with gates for addition","authors":"Y. Takahashi, Y. Kawano, M. Kitagawa","doi":"10.1109/CEC.2003.1299569","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299569","url":null,"abstract":"We investigate a class QNC/sup 0/ (ADD) that is QNC/sup 0/ with gates for addition of two binary numbers, where QNC/sup 0/ is a class consisting of quantum operations computed by constant-depth quantum circuits. We show that QNC/sup 0/(ADD) = QNC/sup 0/(PAR), where QNC/sup 0/(PAR) is QNC/sup 0/ with Toffoli gates of arbitrary fan-in and gates for parity. Moreover, we show that QNC/sup 0/(ADD) = QAC/sup 0/(MUL) = QAC/sup 0/(DIV), where QAC/sup 0/(MUL) and QAC/sup 0/(DIV) are QNC/sup 0/ with Toffoli gates of arbitrary fan-in and gates for multiplication and division respectively. In the classical setting, similar relationships do not hold. These relationships suggest that QNC/sup 0/ /spl subne/ QNC/sup 0/(ADD); that is, the use of gates for addition increases the computational power of constant-depth quantum circuits. To prove QNC/sup 0/ /spl subne/ QNC/sup 0/(ADD), we present a characterization of this relationship by the one-wayness of a permutation that is constructed explicitly. We conjecture that the permutation is one-way, which implies QNC/sup 0/ /spl subne/ QNC/sup 0/(ADD).","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134150781","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299579
U. Paquet, A. Engelbrecht
A new PSO algorithm, the linear PSO (LPSO), is developed to optimise functions constrained by linear constraints of the form Ax = b. A crucial property of the LPSO is that the possible movement of particles through vector spaces is guaranteed by the velocity and position update equations. This property makes the LPSO ideal in optimising linearly constrained problems. The LPSO is extended to the converging linear PSO, which is guaranteed to always find at least a local minimum.
{"title":"A new particle swarm optimiser for linearly constrained optimisation","authors":"U. Paquet, A. Engelbrecht","doi":"10.1109/CEC.2003.1299579","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299579","url":null,"abstract":"A new PSO algorithm, the linear PSO (LPSO), is developed to optimise functions constrained by linear constraints of the form Ax = b. A crucial property of the LPSO is that the possible movement of particles through vector spaces is guaranteed by the velocity and position update equations. This property makes the LPSO ideal in optimising linearly constrained problems. The LPSO is extended to the converging linear PSO, which is guaranteed to always find at least a local minimum.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"159 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113986793","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299903
Maumita Bhattacharya, Guojun Lu
A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluation speeds of the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.
{"title":"DAFHEA: a dynamic approximate fitness-based hybrid EA for optimisation problems","authors":"Maumita Bhattacharya, Guojun Lu","doi":"10.1109/CEC.2003.1299903","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299903","url":null,"abstract":"A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluation speeds of the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528726","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299640
Shengyin Wang, K. Tai
A bit-array representation method for structural topology optimization using the GA is proposed. The importance of design connectivity is further emphasized and a hierarchical violation penalty method is proposed to penalize the violated constraint functions so that the problem of representation degeneracy can be overcome and the GA search can be driven towards the combination of better structural performance, less unusable material and fewer connected objects in the design domain. An identical initialization method is also proposed to test the performance of the GA operators. With the appropriately selected GA operators, the bit-array representation GA is applied to the structural topology optimization problems of minimum weight. Numerical results demonstrate that the present GA can achieve better accuracy with less computational cost and suggest that the GA performance can be significantly improved by handling the design connectivity properly.
{"title":"A bit-array representation GA for structural topology optimization","authors":"Shengyin Wang, K. Tai","doi":"10.1109/CEC.2003.1299640","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299640","url":null,"abstract":"A bit-array representation method for structural topology optimization using the GA is proposed. The importance of design connectivity is further emphasized and a hierarchical violation penalty method is proposed to penalize the violated constraint functions so that the problem of representation degeneracy can be overcome and the GA search can be driven towards the combination of better structural performance, less unusable material and fewer connected objects in the design domain. An identical initialization method is also proposed to test the performance of the GA operators. With the appropriately selected GA operators, the bit-array representation GA is applied to the structural topology optimization problems of minimum weight. Numerical results demonstrate that the present GA can achieve better accuracy with less computational cost and suggest that the GA performance can be significantly improved by handling the design connectivity properly.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116723646","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299831
S. Aupetit, V. Bordeau, N. Monmarché, M. Slimane, G. Venturini
We present how we use an interactive genetic algorithm to find the best parameters to build an artificial art work according to user's aesthetic taste. Ants are used to spread colors on a numerical painting and behave with very simple rules to follow and deposit colors. These rules and colors are considered as parameters for the evolutionary process. This work can be considered as a contribution to naturally inspired artificial art and evolutionary techniques are used to help artists in their creative process.
{"title":"Interactive evolution of ant paintings","authors":"S. Aupetit, V. Bordeau, N. Monmarché, M. Slimane, G. Venturini","doi":"10.1109/CEC.2003.1299831","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299831","url":null,"abstract":"We present how we use an interactive genetic algorithm to find the best parameters to build an artificial art work according to user's aesthetic taste. Ants are used to spread colors on a numerical painting and behave with very simple rules to follow and deposit colors. These rules and colors are considered as parameters for the evolutionary process. This work can be considered as a contribution to naturally inspired artificial art and evolutionary techniques are used to help artists in their creative process.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115011576","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299925
Timothy G. W. Gordon
Traditional circuit design does not scale well to large, complex problems. Nature solves the scalability problem by using a complex mapping implicit in the process of biological development. By modelling this process we aim to improve scalability in evolutionary circuit design. Here we extend our earlier work (Gordon and Bentley, 2002) by demonstrating that evolution can learn and encode useful circuit design abstractions in a developmental process. We go on to present enhanced models of development with improved intercellular communication and show how this improves their ability to generate circuits.
{"title":"Exploring models of development for evolutionary circuit design","authors":"Timothy G. W. Gordon","doi":"10.1109/CEC.2003.1299925","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299925","url":null,"abstract":"Traditional circuit design does not scale well to large, complex problems. Nature solves the scalability problem by using a complex mapping implicit in the process of biological development. By modelling this process we aim to improve scalability in evolutionary circuit design. Here we extend our earlier work (Gordon and Bentley, 2002) by demonstrating that evolution can learn and encode useful circuit design abstractions in a developmental process. We go on to present enhanced models of development with improved intercellular communication and show how this improves their ability to generate circuits.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420528","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299607
Y. Bernstein, Xiaodong Li
Genetic algorithms (GA) have proved to be an effective technique for search and optimization over difficult domains. One common problem for GAs is the phenomenon of premature convergence to suboptimal solutions. We conjecture that premature convergence occurs in part because genetic algorithms lack critical dynamics. This paper proposes a novel algorithm, the genepile evolutionary algorithm, which makes use of the complex spatial dynamics of the sandpile model of self-organized criticality. It is suggested that the critical dynamics of this algorithm make it less prone to getting trapped at local optima. Though the genepile evolutionary algorithm did converge during testing, it has nonetheless proved to be an effective optimization tool, recording good performance across a broad suite of test functions and in many cases substantially outperforming two well-known control algorithms.
{"title":"Critical dynamics in evolutionary algorithms","authors":"Y. Bernstein, Xiaodong Li","doi":"10.1109/CEC.2003.1299607","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299607","url":null,"abstract":"Genetic algorithms (GA) have proved to be an effective technique for search and optimization over difficult domains. One common problem for GAs is the phenomenon of premature convergence to suboptimal solutions. We conjecture that premature convergence occurs in part because genetic algorithms lack critical dynamics. This paper proposes a novel algorithm, the genepile evolutionary algorithm, which makes use of the complex spatial dynamics of the sandpile model of self-organized criticality. It is suggested that the critical dynamics of this algorithm make it less prone to getting trapped at local optima. Though the genepile evolutionary algorithm did converge during testing, it has nonetheless proved to be an effective optimization tool, recording good performance across a broad suite of test functions and in many cases substantially outperforming two well-known control algorithms.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121388297","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299756
C. Mumford
The multiple knapsack problem (MKP) is a popular test-bed for researchers developing new Pareto-based multiobjective evolutionary algorithms. We explore a range of different representations and operators for the MKP, which have been adapted from the single objective case. Results indicate that order-based approaches are superior to binary representations for the problem instances considered here.
{"title":"Comparing representations and recombination operators for the multi-objective 0/1 knapsack problem","authors":"C. Mumford","doi":"10.1109/CEC.2003.1299756","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299756","url":null,"abstract":"The multiple knapsack problem (MKP) is a popular test-bed for researchers developing new Pareto-based multiobjective evolutionary algorithms. We explore a range of different representations and operators for the MKP, which have been adapted from the single objective case. Results indicate that order-based approaches are superior to binary representations for the problem instances considered here.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122899724","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299369
H. Ishibuchi, Shiori Kaige
Multiobjective 0/1 knapsack problems have been used for examining the performance of EMO (evolutionary multiobjective optimization) algorithms in the literature. We demonstrate that their performance on such a test problem strongly depends on the choice of a repair procedure. We show through computational experiments that much better results are obtained from greedy repair based on a weighted scalar fitness function than the maximum profit/weight ratio, which has been often used for ordering items in many studies. This observation explains several reported results in comparative studies about the superiority of EMO algorithms with a weighted scalar fitness function. It is also shown that the performance of EMO algorithms based on Pareto ranking is significantly improved by the use of the weighted scalar fitness function in repair procedures. We also examine randomized greedy repair, where items are ordered based on the profit/weight ratio with respect to a randomly selected knapsack.
{"title":"Effects of repair procedures on the performance of EMO algorithms for multiobjective 0/1 knapsack problems","authors":"H. Ishibuchi, Shiori Kaige","doi":"10.1109/CEC.2003.1299369","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299369","url":null,"abstract":"Multiobjective 0/1 knapsack problems have been used for examining the performance of EMO (evolutionary multiobjective optimization) algorithms in the literature. We demonstrate that their performance on such a test problem strongly depends on the choice of a repair procedure. We show through computational experiments that much better results are obtained from greedy repair based on a weighted scalar fitness function than the maximum profit/weight ratio, which has been often used for ordering items in many studies. This observation explains several reported results in comparative studies about the superiority of EMO algorithms with a weighted scalar fitness function. It is also shown that the performance of EMO algorithms based on Pareto ranking is significantly improved by the use of the weighted scalar fitness function in repair procedures. We also examine randomized greedy repair, where items are ordered based on the profit/weight ratio with respect to a randomly selected knapsack.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126412183","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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