Evolutionary Computation and the Tinkerer's Evolving Toolbox

In nature, variation mechanisms have evolved that permit increasingly rapid and complex adaptations to the environment. Similarly, it may be observed that evolutionary learning systems are adopting increasingly sophisticated variation mechanisms. In this paper, we draw parallels between the adaptation mechanisms in nature and those in evolutionary learning systems. Extrapolating this trend, we indicate an interesting new direction for future work on evolutionary learning systems.

[1]  J. Baldwin.  A New Factor in Evolution , 1896, The American Naturalist.

[2]  Gilles Venturini,et al.  SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts , 1993, ECML.

[3]  Peter Edwards,et al.  The Communication of Inductive Inferences , 1996, ECAI Workshop LDAIS / ICMAS Workshop LIOME.

[4]  D. Thaler,et al.  Microbial genetics: The tinkerer's evolving tool-box , 1997, Nature.

[5]  Helen G. Cobb.  Is the Genetic Algorithm a Cooperative Learner? , 1992, FOGA.

[6]  A. Hoffman.  Arguments on evolution : a paleontologist's perspective , 1989 .

[7]  Kenneth A. De Jong,et al.  On Using Genetic Algorithms to Search Program Spaces , 1987, ICGA.

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

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

[10]  David B. Fogel,et al.  Evolutionary programming: an introduction and some current directions , 1994 .

[11]  Gerhard Weiß.  Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments , 1997, Lecture Notes in Computer Science.

[12]  Julian Huxley,et al.  Evolution as a Process , 1958 .

[13]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[14]  Kwong-Sak Leung,et al.  Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System , 1995, IEEE Expert.

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

[16]  David E. Goldberg,et al.  Genetic and evolutionary algorithms come of age , 1994, CACM.

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

[18]  Filippo Neri,et al.  Search-Intensive Concept Induction , 1995, Evolutionary Computation.

[19]  Bernardo A. Huberman,et al.  The performance of cooperative processes , 1990 .

[20]  D. Fogel.  Applying evolutionary programming to selected traveling salesman problems , 1993 .

[21]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[22]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[23]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[24]  Shozo Naito,et al.  Inductive Logic Programming Based on Genetic Algorithm , 1995, ASIAN.

[25]  J. Murray,et al.  The Evolutionary Process , 1985 .

[26]  Tim Finin,et al.  KQML - A Language and Protocol for Knowledge and Information Exchange , 1994 .

[27]  Richard M. Friedberg,et al.  A Learning Machine: Part I , 1958, IBM J. Res. Dev..

[28]  Carlo Ghezzi,et al.  Programming language concepts , 1982 .

[29]  R. Lenski,et al.  Evolution of high mutation rates in experimental populations of E. coli , 1997, Nature.