On Interactive Evolution Strategies

In this paper we discuss Evolution Strategies within the context of interactive optimization. Different modes of interaction will be classified and compared. A focus will be on the suitability of the approach in cases, where the selection of individuals is done by a human user based on subjective evaluation. We compare the convergence dynamics of different approaches and discuss typical patterns of user interactions observed in empirical studies. The discussion of empirical results will be based on a survey conducted via the world wide web. A color (pattern) redesign problems from literature will be adopted and extended. The simplicity of the chosen problems allowed us to let a larger number of people participate in our study. The amount of data collected makes it possible to add statistical support to our hypothesis about the performance and behavior of different Interactive Evolution Strategies and to figure out high-performing instantiations of the approach. The behavior of the user was also compared to a deterministic selection of the best individual by the computer. This allowed us to figure out how much the convergence speed is affected by noise and to estimate the potential for accelerating the algorithm by means of advanced user interaction schemes.

[1]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[2]  Michael Herdy,et al.  Evolution Strategies with Subjective Selection , 1996, PPSN.

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

[4]  Günter Rudolph.  On Interactive Evolutionary Algorithms and Stochastic Mealy Automata , 1996, PPSN.

[5]  Bogdan Filipič,et al.  An Interactive Genetic Algorithm for Controller Parameter Optimization , 1993 .

[6]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[7]  Damon Horowitz,et al.  Generating Rhythms with Genetic Algorithms , 1994, AAAI.

[8]  Hans-Paul Schwefel,et al.  Parallel Problem Solving from Nature — PPSN IV , 1996, Lecture Notes in Computer Science.

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

[10]  Hans-Georg Beyer,et al.  The Theory of Evolution Strategies , 2001, Natural Computing Series.

[11]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[12]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[13]  James Reason,et al.  Human Error , 1990 .

[14]  W. Banzhaf.  C2.10 Interactive Evolution , 1997 .

[15]  Gregory D. Abowd,et al.  Human-Computer Interaction (3rd Edition) , 2003 .

[16]  I. C. Parmee,et al.  Cluster-oriented genetic algorithms to support interactive designer/evolutionary computing systems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[17]  John R. Anderson.  Cognitive Psychology and Its Implications , 1980 .

[18]  P. Angeline.  Evolving fractal movies , 1996 .