Random Heuristic Search

There is a developing theory of growing power which, at its current stage of development (indeed, for a number of years now), speaks to qualitative and quantitative aspects of search strategies. Although it has been specialized and applied to genetic algorithms, its implications and applicability are far more general. This paper deals with the broad outlines of the theory, introducing basic principles and results rather than analyzing or specializing to particular algorithms. A few specific examples are included for illustrative purposes, but the theory's basic structure, as opposed to applications, remains the focus.

[1]  Alden H. Wright,et al.  Simple Genetic Algorithms with Linear Fitness , 1994, Evolutionary Computation.

[2]  D. Chillingworth.  THE GENERAL TOPOLOGY OF DYNAMICAL SYSTEMS , 1995 .

[3]  Michael D. Vose,et al.  Modeling Simple Genetic Algorithms , 1992, FOGA.

[4]  Gunar E. Liepins,et al.  Punctuated Equilibria in Genetic Search , 1991, Complex Syst..

[5]  M. Arbib,et al.  Arrows, Structures, and Functors: The Categorical Imperative , 1975 .

[6]  Michael D. Vose.  Modeling Alternate Selection Schemes For Genetic Algorithms , 2001 .

[7]  John H. Holland,et al.  When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[8]  Michael D. Vose,et al.  The Genetic Algorithm Fractal , 1993, Evolutionary Computation.

[9]  Michael D. Vose,et al.  Formalizing Genetic Algorithms , 1991 .

[10]  Patrick D. Surry,et al.  Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective , 1995, Computer Science Today.

[11]  Siddhartha Bhattacharyya,et al.  General Cardinality Genetic Algorithms , 1997, Evolutionary Computation.

[12]  Michael D. Vose.  Logarithmic Convergence of Random Heuristic Search , 1996, Evolutionary Computation.

[13]  Adam Prügel-Bennett,et al.  Maximum Entropy Analysis of Genetic Algorithm Operators , 1995, Evolutionary Computing, AISB Workshop.

[14]  M. Vose,et al.  Random heuristic search: applications to GAs and functions of unitation , 2000 .

[15]  J. H. Wilkinson.  The algebraic eigenvalue problem , 1966 .

[16]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[17]  Adam Prügel-Bennett,et al.  A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[18]  Avi Wigderson,et al.  An Analysis of a Simple Genetic Algorithm , 1991, ICGA.

[19]  Michael D. Vose.  What are Genetic Algorithms? A Mathematical Prespective , 1999 .

[20]  Jonathan E. Rowe,et al.  Population Fixed-Points for Functions of Unitation , 1998, FOGA.

[21]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[22]  Melanie Mitchell,et al.  Finite populations induce metastability in evolutionary search , 1997 .

[23]  Alden H. Wright,et al.  Finiteness of the Fixed Point Set for the Simple Genetic Algorithm , 1995, Evolutionary Computation.

[24]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[25]  John H. Holland,et al.  Adaptation in natural and artificial systems , 1975 .