{"title":"Evolving constrained mean-VaR efficient frontiers","authors":"Haken K. Jevne, P. Haddow, A. Gaivoronski","doi":"10.1109/CEC.2012.6252907","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252907","url":null,"abstract":"Value-at-Risk - an industry standard risk measure; may be applied to assess and optimize a portfolio of assets. However, traditional optimization software does not provide for value-at-risk optimization. One solution is to apply evolutionary techniques to search for optimal solutions. However, to increase the realism in evolutionary solutions, it is important to consider the inclusion of realistic real-world constraints and to further consider the effect of the initialization scheme on the results achievable. Two techniques are investigated in this work. The key technique is multi-objective differential evolution (MODE) which is applied together with an adapted initialization scheme to search for VaR-optimal portfolios in the presence of real-world constraints. Further, NSGA-II - a more established multi-objective optimization technique; is implemented and extended with real world constraints and a refined initialization scheme, so as to compare the benefits of the MODE technique in the light of a refined NSGA-II technique and highlight the benefits of such refinements on NSGA-II itself.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000221","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}
{"title":"Local cooperation delivers global optimization","authors":"Zhou Wu, Lu Xu, T. Chow, Mingbo Zhao","doi":"10.1109/CEC.2012.6256548","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256548","url":null,"abstract":"The cooperation behaviors existing in the animal and human being societies, have been modeled for the numerical optimization, but the local cooperation has not been modeled separately in optimization problems. In this paper the local cooperation is newly modeled as Neighborhood Field Model (NFM). Based on NFM, a new optimization technique called Neighborhood Field Optimization algorithm (NFO) is firstly proposed to deliver global optimization. In NFO, each individual is attracted by its superior neighbor and repulsed by its inferior neighbor to search a better solution. In this paper, NFO is compared with certain algorithms under twelve different benchmark functions. The results show that NFO can outperform them on multimodal functions in the respect of accuracy, effectiveness and robustness. It also can be noted that the cooperation behavior can play a dominant role in the optimization algorithm separately.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128976519","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}
{"title":"A modified brain storm optimization","authors":"Zhi-hui Zhan, Jun Zhang, Yu-hui Shi, Hai-Lin","doi":"10.1109/CEC.2012.6256594","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256594","url":null,"abstract":"Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by human creative problem solving process. Human being is the most intelligent organism in the world and the brainstorming process popularly used by them has been demonstrated to be a significant and promising way to create great ideas for problem solving. BSO transplants the brainstorming process in human being into optimization algorithm design and gains successes. BSO generally uses the grouping, replacing, and creating operators to produce ideas as many as possible to approach the problem global optimum generation by generation. In this paper, we propose two novel designs to enhance the conventional BSO performance. The first design of the modified BSO (MBSO) is that it uses a simple grouping method (SGM) in the grouping operator instead of the clustering method to reduce the algorithm computational burden. The second design is that MBSO uses a novel idea difference strategy (IDS) in the creating operator instead of the Gaussian random strategy. The IDS not only contains open minded element to avoid the ideas being trapped by local optima, but also can match the search environment to create better new ideas for problem solving. Experiments have been conducted to illustrate the effectiveness and efficiency of the MBSO algorithm. Moreover, the contributions of SGM and IDS are investigated to show how and why MBSO can perform better than BSO.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122379593","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}
{"title":"Hybrid Bacterial Iterated Greedy heuristics for the Permutation Flow Shop Problem","authors":"K. Balázs, Z. Horváth, L. Kóczy","doi":"10.1109/CEC.2012.6256167","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256167","url":null,"abstract":"This paper proposes approaches for combining the Iterated Greedy (IG) technique, as a presently state-of-the-art method, with a recently proposed adapted version of the Bacterial Evolutionary Algorithm (BEA) in order to efficiently solve the Permutation Flow Shop Problem. The obtained techniques are evaluated via simulation runs carried out on the well-known Taillard's benchmark problem set. Based on the experimental results the hybrid methods are compared to each other and to the original techniques (i.e. to the original IG and BEA algorithms).","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130557890","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}
{"title":"The evolution of cooperation via stigmergic interactions","authors":"R. Chiong, M. Kirley","doi":"10.1109/CEC.2012.6256474","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256474","url":null,"abstract":"We study the evolution of cooperation in a population of agents playing the N-player Prisoner's Dilemma game via stigmergic interactions. Here, agent decision making is guided by a shared pheromone table. Actions are played at each time step and a trace (or signal) corresponding to the rewards received is recorded in this shared table. Subsequent actions are then determined probabilistically using the shared information. Comprehensive Monte Carlo simulation experiments show that the stigmergy-based mechanism is able to promote cooperation despite the fact that the make-up of the interacting groups is continually changing. A direct comparison with a genetic algorithm-based N-player model confirms that the extent of cooperative behaviour achieved is significantly higher across a wide range of cost-to-benefit ratios. In the concluding remarks, we highlight the real-world implications of stigmergic interactions.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130459320","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}
{"title":"A composite heuristic for the no-wait flow shop scheduling","authors":"K. Gao, P. N. Suganthan, Z. Bao","doi":"10.1109/CEC.2012.6252932","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252932","url":null,"abstract":"Heuristics that explore specific characteristics of the problem are essential to find good solutions in limited computational time for many practical systems. This paper first presents a constructive heuristic, namely improved standard deviation heuristic (ISDH), by combining the standard deviation heuristic (SDH) with the procedure of effective double-job-insert-operator. Then, a composite heuristic, improved standard deviation heuristic with iteration (ISDHI), is proposed using the iteration operator to improve the solutions of the ISDH. Extensive computational experiments are carried out based on a set of well-known flow shop benchmark instances that are considered as no-wait flow shop scheduling instances. Computational results and comparisons show that the ISDHI performs significantly better than the existing ones, and the ISDHI heuristic further improves the proposed constructive heuristics for no-wait flow shop scheduling problem with total flow time criterion.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125455338","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}
{"title":"Evolutionary algorithms for supertree search","authors":"S. Ficici, E. Liu, G. Fogel","doi":"10.1109/CEC.2012.6256460","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256460","url":null,"abstract":"Phylogenetic inference of the history of life on Earth continues to be a major effort of evolutionary biology. Such inference can be accomplished through the use of individual genes, sets of genes, or complete genomes. While the latter may provide the most robust description of the true phylogenetic history, the computational demands of complete genome comparison and phylogenetic construction is daunting. Thus most researchers are left using sets of conserved genes for the resolution of a common phylogeny (what is termed a “supertree” search). However as the number of taxa increases or as the number of source trees used in construction of a supertree increases, the number of possible supertree solutions increases tremendously. This requires consideration of alternate methods to search this space efficiently such as those that use stochastic methods. Here for the first time we present a method for supertree search using evolutionary algorithms and evaluate its utility on a set of derived supertree problems with 50 taxa. The results indicate the utility of this approach and offer opportunities for future refinement.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902579","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}
{"title":"Immune memory with associativity: Perspectives on dynamical systems","authors":"C. Ou, C. Ou","doi":"10.1109/CEC.2012.6256646","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256646","url":null,"abstract":"Immune memory can be considered as an equilibrium state of immune network system with nonlinear dynamical behavior. The rapid response of immune systems to the second-time antigen is owing to the stable structure of memory state forming by a closed loop of the idiotypic immune network. A dynamical system of cell population is proposed which explains how the memory state is formed and stabilized in the immune network. stability analysis of antibody dynamics also explains the associativity of immune memory.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385233","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}
{"title":"Multi-objective Invasive Weed Optimization algortihm for clustering","authors":"Ruochen Liu, Xiao Wang, Yangyang Li, Xiangrong Zhang","doi":"10.1109/CEC.2012.6256540","DOIUrl":"https://doi.org/10.1109/CEC.2012.6256540","url":null,"abstract":"In this paper, we proposed a new approach to solve the clustering problem in which the cluster number is uncertainty. It utilizes IWO (Invasive Weed Optimization) algorithm to optimize two fuzzy clustering objective function simultaneously, and a variable-length real-coded scheme has been adopted, the variable length weed encodes the cluster centers with variable numbers. In order to keep the diversity of the weeds, we introduce a new mechanism called feedback update mechanism to update the individuals which the corresponding number of cluster centers has been eliminated in one generation. Finally, the Silhouette index is used to select the best solution. The algorithm is used to cluster 15 artificial data sets and 4 real life data sets and shows good performance.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126417434","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}
Yanghui Wu, J. Mccall, D. Corne, Olivier Regnier-Coudert
{"title":"Landscape analysis for hyperheuristic Bayesian Network structure learning on unseen problems","authors":"Yanghui Wu, J. Mccall, D. Corne, Olivier Regnier-Coudert","doi":"10.1109/CEC.2012.6252964","DOIUrl":"https://doi.org/10.1109/CEC.2012.6252964","url":null,"abstract":"Bayesian network (BN) structure learning is an NP hard problem. Search and score algorithms are one of the main approaches proposed for learning BN structure from data. Previous research has shown that the relative performances of such algorithms are problem dependent and that fitness landscape analysis can be used to characterize the difficulty of the search for different scoring functions. In this paper, we construct a classifier based on fitness landscape analysis and receiver operating characteristic curves. The classifier labels search landscapes with the most suitable scoring function. We train the classifier on a number of standard benchmark functions. The classifier forms the basis for a selective hyperheuristic algorithm. This uses an initial landscape analysis stage to select a scoring function using the classifier. The hyperheuristic algorithm is tested on a distribution of unseen problems based on mutations of the standard benchmarks. Our results establish that the hyperheuristic performs better than a uniformly random scoring function selection approach that omit the landscape analysis stage. Therefore the effects on performance of problem-dependency can be significantly reduced.","PeriodicalId":376837,"journal":{"name":"2012 IEEE Congress on Evolutionary Computation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123015719","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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