Exploring the Evolutionary Details of a Feasible-Infeasible Two-Population GA

A two-population Genetic Algorithm for constrained optimization is exercised and analyzed. One population consists of feasible candidate solutions evolving toward optimality. Their infeasible but promising offspring are transferred to a second, infeasible population. Four striking features are illustrated by executing challenge problems from the literature. First, both populations evolve essentially optimal solutions. Second, both populations actively exchange offspring. Third, beneficial genetic materials may originate in either population, and typically diffuse into both populations. Fourth, optimization vs. constraint tradeoffs are revealed by the infeasible population.

[1]  Zbigniew Michalewicz,et al.  Evolutionary algorithms for constrained engineering problems , 1996, Computers & Industrial Engineering.

[2]  Steven Orla Kimbrough,et al.  Exploring a financial product model with a two-population genetic algorithm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[3]  Steven Orla Kimbrough,et al.  Exploring a Two-Population Genetic Algorithm , 2003, GECCO.

[4]  R. Haftka,et al.  Improved genetic algorithm for minimum thickness composite laminate design , 1995 .

[5]  C. Floudas.  Handbook of Test Problems in Local and Global Optimization , 1999 .

[6]  Steven Orla Kimbrough,et al.  Exploring A Two-market Genetic Algorithm , 2002, GECCO.

[7]  Steven Orla Kimbrough,et al.  On heuristic mapping of decision surfaces for post-evaluation analysis , 1997, Proceedings of the Thirtieth Hawaii International Conference on System Sciences.

[8]  Ming Yuchi,et al.  Grouping-based evolutionary algorithm: seeking balance between feasible and infeasible individuals of constrained optimization problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[9]  Aharon Ben-Tal,et al.  Global minimization by reducing the duality gap , 1994, Math. Program..

[10]  F. Taddei,et al.  Role of mutator alleles in adaptive evolution , 1997, Nature.

[11]  X. M. Martens,et al.  Comparison of Generalized Geometric Programming Algorithms , 1978 .

[12]  Richard E. Lenski,et al.  Evolution of competitive fitness in experimental populations of E. coli: What makes one genotype a better competitor than another? , 1998, Antonie van Leeuwenhoek.

[13]  Raphael T. Haftka,et al.  A Segregated Genetic Algorithm for Constrained Structural Optimization , 1995, ICGA.

[14]  Rick Hesse.  A Heuristic Search Procedure for Estimating a Global Solution of Nonconvex Programming Problems , 1973, Oper. Res..

[15]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[16]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[17]  R. Lenski,et al.  Parallel changes in gene expression after 20,000 generations of evolution in Escherichia coli , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[18]  D. Fogel,et al.  Advanced Algorithms and Operators , 1999 .

[19]  John E. Beasley,et al.  A genetic algorithm for the generalised assignment problem , 1997, Comput. Oper. Res..

[20]  Steven O. Kimbrough,et al.  On Post-Evaluation Analysis: Candle-Lighting and Surrogate Models , 1993 .

[21]  John E. Beasley,et al.  A Genetic Algorithm for the Multidimensional Knapsack Problem , 1998, J. Heuristics.

[22]  X. M. Martens,et al.  Comparison of generalized geometric programming algorithms , 1978 .

[23]  X. Yuan,et al.  Une méthode d'optimisation non linéaire en variables mixtes pour la conception de procédés , 1988 .

[24]  C. Ofria,et al.  Evolution of digital organisms at high mutation rates leads to survival of the flattest , 2001, Nature.

[25]  Steven Orla Kimbrough,et al.  Exploring the Evolutionary Details of a Two-Population Genetic Algorithm , 2004 .

[26]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .