Philippe Collard

A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming

We present an approach to genetic programming difficulty based on a statistical study of program fitness landscapes. The fitness distance correlation is used as an indicator of problem hardness and we empirically show that such a statistic is adequate in nearly all cases studied here. However, fitness distance correlation has some known problems and these are investigated by constructing an artificial landscape for which the correlation gives contradictory indications. Although our results confirm the usefulness of fitness distance correlation, we point out its shortcomings and give some hints for improvement in assessing problem hardness in genetic programming.

Fitness Clouds and Problem Hardness in Genetic Programming

This paper presents an investigation of genetic programming fitness landscapes. We propose a new indicator of problem hardness for tree-based ge- netic programming, called negative slope coefficient, based on the concept of fitness cloud. The negative slope coefficient is a predictive measure, i.e. it can be calculated without prior knowledge of the global optima. The fitness cloud is gener- ated via a sampling of individuals obtained with the Metropolis-Hastings method. The reliability of the negative slope coefficient is tested on a set of well known and representative genetic programming benchmarks, comprising the binomial-3 problem, the even parity problem and the artificial ant on the Santa Fe trail.

Negative slope coefficient: a measure to characterize genetic programming fitness landscapes

Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal trees.

Fitness distance correlation in structural mutation genetic programming

A new kind of mutation for genetic programming based on the structural distance operators for trees is presented in this paper. We firstly describe a new genetic programming process based on these operators (we call it structural mutation genetic programming). Then we use structural distance to calculate the fitness distance correlation coefficient and we show that this coefficient is a reasonable measure to express problem difficulty for structural mutation genetic programming for the considered set of problems, i.e. unimodal trap functions, royal trees and MAX problem.

Where are bottlenecks in NK fitness landscapes

Usually the offspring-parent fitness correlations is used to visualize and analyze some characteristics of fitness landscapes such as evolvability. In this paper, we introduce a more general representation of this correlation, the fitness cloud (FC). We use the bottleneck metaphor to emphasis fitness levels in landscape that cause local search process to slow down. For a local search heuristic such as hill-climbing or simulated annealing, FC allows one to visualize the bottleneck and neutrality of landscapes. To confirm the relevance of the FC representation we show where the bottlenecks are in the well-known NK fitness landscape and also how to use neutrality information from the FC to combine some neutral operator with local search heuristic.

Eye-tracking evolutionary algorithm to minimize user fatigue in IEC applied to interactive one-max problem

In this paper, we describe a new algorithm that consists in combining an eye-tracker for minimizing the fatigue of a user during the evaluation process of Interactive Evolutionary Computation. The approach is then applied to the Interactive One-Max optimization problem.

Two models of immunization for time dependent optimization

This paper investigates the relevance of the immune system metaphor to time-dependent optimization (TDO). First, we review previous results underlining the over-average reactiveness and robustness featured by Sais (Simple Artificial Immune System) in comparison to well-known evolutionist approaches. Then, we evaluate its immunization capability (i.e. improving its robustness to previously encountered optima) and eventually propose Yasais (Yet Another Simple Artificial Immune System).

Maximum homologous crossover for linear genetic programming

We introduce a new recombination operator, the Maximum Homologous Crossover for Linear Genetic Programming. In contrast to standard crossover, it attempts to preserve similar structures from parents, by aligning them according to their homology, thanks to an algorithm used in Bio-Informatics. To highlight disruptive effects of crossover operators, we introduce the Royal Road landscapes and the Homology Driven Fitness problem, for Linear Genetic Programming. Two variants of the new crossover operator are described and tested on this landscapes. Results show a reduction in the bloat phenomenon and in the frequency of deleterious crossovers.

An Evolutionary Approach for Time Dependent Optimization

Many real-world problems involve measures of objectives that may be dynamically optimized. The application of evolutionary algorithms, such as genetic algorithms, in time dependent optimization is currently receiving growing interest as potential applications are numerous ranging from mobile robotics to real time process command. Moreover, constant evaluation functions skew results relative to natural evolution so that it has become a promising gap to combine effectiveness and diversity in a genetic algorithm. This paper features both theoretical and empirical analysis of the behavior of genetic algorithms in such an environment. We present a comparison between the effectivenss of traditional genetic algorithm and the dual genetic algorithm which has revealed to be a particularly adaptive tool for optimizing a lot of diversified classes of functions. This comparison has been performed on a model of dynamical environments which characteristics are analyzed in order to establish the basis of a testbed for further experiments. We also discuss fundamental properties that explain the effectiveness of the dual paradigm to manage dynamical environments.

Genetic operators in a dual genetic algorithm

It is not clear that the current distinction between crossover and mutation is necessary. We show that it is possible to implement one and only one general operator which can specialize crossover or mutation operators. We investigate this alternative. Our approach consists in inserting doubles in the population of chromosomes. This article argues that explicit mutations are unnecessary. Indeed, in dGAs without a mutation operator, chromosomes undergo the mutation effect. The dual genetic search provides a source of power for searching in a changing environment. Within this paper, a first effort is presented towards incorporating the feature of self-adaptation into GAs by using adaptive mutation rates. Finally, we study the effects of explicit mutation on a dual search space. We show that a contraction of the Hamming distance is induced from mutation. As a consequence, a dGA allows to increase the capabilities of evolution on rugged fitness landscapes.