On Multi-Objective Evolutionary Algorithms

In this chapter Multi-Objective Evolutionary Algorithms (MOEAs) are introduced and some details discussed. A presentation of some of the concepts in which this type of algorithms are based on is given. Then, a summary of the main algorithms behind these approaches and their applications is provided, together with a brief discussion including their advantages and disadvantages, degree of applicability, and some known applications. Finally, future trends in this area and some possible paths for future research are pointed out.

[1]  Bernhard Sendhoff,et al.  Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Joshua D. Knowles,et al.  Multiobjective Optimization in Bioinformatics and Computational Biology , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[3]  Philippe Vincke,et al.  Analysis of multicriteria decision aid in Europe , 1986 .

[4]  Bernard De Baets,et al.  A spatial approach to forest‐management optimization: linking GIS and multiple objective genetic algorithms , 2006, Int. J. Geogr. Inf. Sci..

[5]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[6]  Jean-Charles Billaut,et al.  Multicriteria scheduling problems: a survey , 2001, RAIRO Oper. Res..

[7]  Kalyanmoy Deb,et al.  Portfolio optimization with an envelope-based multi-objective evolutionary algorithm , 2009, Eur. J. Oper. Res..

[8]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Armin Scholl,et al.  State-of-the-art exact and heuristic solution procedures for simple assembly line balancing , 2006, Eur. J. Oper. Res..

[11]  Khalid Iqbal,et al.  Automated Data Mining Techniques: A Critical Literature Review , 2009, 2009 International Conference on Information Management and Engineering.

[12]  Jin Li,et al.  Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[15]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[16]  David Beasley,et al.  Possible applications of evolutionary computation , 2018, Evolutionary Computation 1.

[17]  Detlef Seese,et al.  FINANCIAL APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS: RECENT DEVELOPMENTS AND FUTURE RESEARCH DIRECTIONS , 2004 .

[18]  Darryl Charles,et al.  Machine learning in digital games: a survey , 2008, Artificial Intelligence Review.

[19]  B. Babu,et al.  Differential evolution for multi-objective optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[20]  Carlos A. Coello Coello,et al.  Applications of multi-objective evolutionary algorithms in economics and finance: A survey , 2007, 2007 IEEE Congress on Evolutionary Computation.

[21]  Carlos M. Fonseca,et al.  Methodology to select solutions from the pareto-optimal set: a comparative study , 2007, GECCO '07.

[22]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  Richard F. Hartl,et al.  Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection , 2004, Ann. Oper. Res..

[25]  Lily Rachmawati,et al.  Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[26]  António Gaspar-Cunha,et al.  A Hybrid Multi-Objective Evolutionary Algorithm Using an Inverse Neural Network , 2004, Hybrid Metaheuristics.

[27]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[28]  Shu-Cherng Fang,et al.  An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..

[29]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[30]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[31]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[32]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[33]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[34]  Richard F. Hartl,et al.  Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection , 2006, Eur. J. Oper. Res..

[35]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[36]  António Gaspar-Cunha,et al.  RPSGAe - Reduced Pareto Set Genetic Algorithm: Application to Polymer Extrusion , 2004, Metaheuristics for Multiobjective Optimisation.

[37]  Mikkel T. Jensen,et al.  Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms , 2003, IEEE Trans. Evol. Comput..

[38]  Carlos M. Fonseca,et al.  Evolutionary Multi-Objective Robust Optimization , 2008 .

[39]  N. Mort,et al.  Evolving knowledge for the solution of clustering problems in cellular manufacturing , 2004 .

[40]  António Gaspar-Cunha,et al.  Robustness in multi-objective optimization using evolutionary algorithms , 2008, Comput. Optim. Appl..

[41]  Christos Dimopoulos,et al.  A review of evolutionary multiobjective optimization applications in the area of production research , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[42]  M. Fernanda P. Costa,et al.  Multi-objective memetic algorithm using pattern search filter methods memetic multi-objective algorithm , 2009 .

[43]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[44]  Detlef Seese,et al.  Modern Heuristics for Finance Problems: A Survey of Selected Methods and Applications , 2004 .

[45]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[46]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[47]  Mario Vanhoucke,et al.  The Electromagnetism Meta-heuristic Applied to the Resource-Constrained Project Scheduling Problem , 2005, Artificial Evolution.

[48]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[49]  Shu-Heng Chen.  Evolutionary Computation in Economics and Finance , 2002 .

[50]  Pedro Oliveira,et al.  Dimension reduction in multiobjective optimization , 2007 .

[51]  José M. Molina López,et al.  Effective Evolutionary Algorithms for Many-Specifications Attainment: Application to Air Traffic Control Tracking Filters , 2009, IEEE Transactions on Evolutionary Computation.

[52]  A. Nagar,et al.  Multiple and bicriteria scheduling : A literature survey , 1995 .

[53]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[54]  Thomas L. Saaty,et al.  Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation , 1990 .

[55]  H. Kunzi,et al.  Lectu re Notes in Economics and Mathematical Systems , 1975 .

[56]  Derek W. Bunn,et al.  Multiple Criteria Problem Solving , 1979 .

[57]  Svetlozar T. Rachev,et al.  Handbook of computational and numerical methods in finance , 2004 .

[58]  Francisco Herrera,et al.  A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP , 2007, Eur. J. Oper. Res..

[59]  Andreas C. Nearchou,et al.  Multi-objective balancing of assembly lines by population heuristics , 2008 .

[60]  Deming Lei,et al.  Multi-objective production scheduling: a survey , 2009 .

[61]  Hussein A. Abbass,et al.  The Pareto Differential Evolution Algorithm , 2002, Int. J. Artif. Intell. Tools.

[62]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[63]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[64]  Moshe Sipper,et al.  Evolutionary computation in medicine: an overview , 2000, Artif. Intell. Medicine.

[65]  Rajeev Kumar,et al.  ON MACHINE LEARNING WITH MULTIOBJECTIVE GENETIC OPTIMIZATION , 2004 .

[66]  C. Dimopoulos,et al.  Explicit consideration of multiple objectives in cellular manufacturing , 2007 .

[67]  H. Ishibuchi,et al.  Multi-objective genetic algorithm and its applications to flowshop scheduling , 1996 .

[68]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[69]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[70]  Carlos M. Fonseca,et al.  Selection of solutions in a multi-objective environment: polymer extrusion a case study , 2007 .

[71]  Yaochu Jin,et al.  Multi-Objective Machine Learning , 2006, Studies in Computational Intelligence.

[72]  Danny J. Johnson,et al.  Empirical findings on manufacturing cell design , 2000 .

[73]  Wenyin Gong,et al.  An efficient multiobjective differential evolution algorithm for engineering design , 2009 .

[74]  Stephen T. Newman,et al.  A review of the modern approaches to multi-criteria cell design , 2000 .

[75]  Armin Scholl,et al.  Balancing and Sequencing of Assembly Lines , 1995 .

[76]  Yeongho Kim,et al.  Genetic algorithms for assembly line balancing with various objectives , 1996 .

[77]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..