Dept. of ECE, Univ. of California, San Diego

9500 Gilman Drive                           

La Jolla, CA 92093-4007                     

Ph: (619) 534-5935; Fax: (619) 534-1004     

kchellap@ece.ucsd.edu  

1. Fitness Distributions in Evolutionary Computation: Analysis of Local Extrema in the Continuous Domain

Kumar Chellapilla and David B. Fogel

The design of evolutionary computations based on schema processing, minimizing expected losses, and emphasizing certain genetic operators has failed to provide robust optimization performance. Recently, fitness distribution analysis have been proposed as an alternative tool for exploring operator behavior and designing efficient evolutionary computations. For example, the step size of a single parent variation operator determines the corresponding probability of finding better solutions and the expected improvement that will be obtained. This paper analyses the utility of Gaussian, Cauchy, and Mean mutation operators when a parent is located on a local extrema of a continuous objective function that is to be optimized.

2. Towards Evolution of Modular Finite State Machines using Fitness Distributions

David Czarnecki

For a given representations in an evolutionary computation, there are a number of variation operators that can be applied to existing solutions to create new solutions. These variation operators can be classified into two broad categories, exploratory and exploitative operators. Exploratory operators allow for the traversal of a given search space. Exploitative operators may induce behavior that uses the current location in the fitness landscape to move a solution towards a more optimal region in the neighboring search space. Fitness Distributions is a recent technique to assess the viability and quality of such variation operators. This technique is applied to the evolution of modular and non-modular finite state machines. Experiments are conducted on a number of tracking and control problems. Discussion is directed towards assessing the overall effectiveness of operators for such machines.

3. Using Fitness Distributions to Improve the Evolution of Learning Structures

Christian Igel, Martin Kreutz, and Peter Stagge

The analysis of features of the fitness distribution (FD) [3,4] is used to improve the performance of three different structure optimization algorithms by guiding the choice and design of the used variation operators. All three algorithms employ hybrid approaches where an evolutionary algorithm (EA) is used for structure optimization in conjunction with gradient based learning strategies for parameter adaptation.

4. On Functions With A Given Fitness--Distance Relation

Leila Kallel, Bart Naudts and Marc Schoenauer

Recent work stresses the limitations of fitness distance correlation (FDC) as an indicator of landscape difficulty for genetic algorithms (GAs). Realizing that the fitness distance correlation (FDC) value cannot be reliably related to landscape difficulty, this paper investigates whether an interpretation of the correlation plot can yield reliable information about the behavior of the GA. Our approach is as follows: A generic method for constructing fitness functions which share the same fitness versus distance-to-optimum relation (FD relation) is presented. Special attention is given to FD relations which show no local optimum in the correlation plot, as is the case for the relation induced by Horn's longpath. A tentative taxonomy is proposed for he different types of GA behavior found within a class of fitness functions with a common correlation plot. Finally, the behavior of the GA is shown to be very sensitive to small modifications of the fitness--distance relation.

5. Empirical Study Of Two Classes Of Bit Mutation Operators In Evolutionary Computation

Hemanth Birru

Bit mutation operators are widely used in evolutionary computations (EC) that adopt a binary representation. Most commonly, one or more randomly selected bits are flipped with some probability. The success of such a variation operator invariably depends on the location and number of bits being varied in the chromosome. Given an objective function and a candidate parent, bit mutations in some locations will provide an improvement in fitness whereas no improvement will result when mutations are applied to other locations. Two classes of bit mutation operators are defined and the relationship between the probability of improvement in fitness and the expected improvement obtained is studied. Such a study would help in designing better mutation operators to obtain faster convergence in EC methods using a binary representation.