Combinations of simulation and Evolutionary Algorithms in management science and economics

Evolutionary Algorithms are robust search methods that mimic basic principles of evolution. We discuss different combinations of Evolutionary Algorithms and the versatile simulation method resulting in powerful tools not only for complex decision situations but explanatory models also. Realised and suggested applications from the domains of management and economics demonstrate the relevance of this approach. In a practical example three EA-variants produce better results than two conventional methods when optimising the decision variables of a stochastic inventory simulation. We show that EA are also more robust optimisers when only few simulations of each trial solution are performed. This characteristic may be used to reduce the generally higher CPU-requirements of population-based search methods like EA as opposed to point-based traditional optimisation techniques.

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

[2]  Jörg Biethahn.  Optimierung und Simulation , 1978 .

[3]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[4]  J. D. Schaffer,et al.  Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition) , 1984 .

[5]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

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

[7]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[8]  Paul Thagard,et al.  Induction: Processes Of Inference , 1989 .

[9]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[11]  Stephanie Forrest,et al.  Emergent computation: self-organizing, collective, and cooperative phenomena in natural and artificial computing networks , 1990 .

[12]  Thomas Bäck,et al.  Genetic Algorithms and Evolution Strategies - Similarities and Differences , 1990, PPSN.

[13]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[14]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[15]  L. Darrell Whitley,et al.  A Comparison of Genetic Sequencing Operators , 1991, ICGA.

[16]  David B. Fogel,et al.  Evolving artificial intelligence , 1992 .

[17]  Henri Pierreval,et al.  Rule-based simulation metamodels , 1992 .

[18]  Thomas Bäck,et al.  The Interaction of Mutation Rate, Selection, and Self-Adaptation Within a Genetic Algorithm , 1992, PPSN.

[19]  W. Arthur.  On Learning and Adaptation in the Economy , 1992 .

[20]  Robert H. Storer,et al.  Local Search in Problem and Heuristic Space for Job Shop Scheduling Genetic Algorithms , 1992 .

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

[22]  H. Altay Güvenir,et al.  A Genetic Algorithm for Classification by Feature Partitioning , 1993, ICGA.

[23]  J. Koza.  Simultaneous Discovery of Reusable Detectors and S u b r o u t i n e s Using Genetic Programming , 1993 .

[24]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[25]  Volker Nissen,et al.  Solving the quadratic assignment problem with clues from nature , 1994, IEEE Trans. Neural Networks.

[26]  Constantino Tsallis,et al.  Optimization by Simulated Annealing: Recent Progress , 1995 .

[27]  L. Marengo.  Structure, Competence and Learning in an Adaptive Model of the Firm , 1996 .