Deceptiveness and Neutrality - the ND family of fitness landscapes

TL;DR

This paper introduces a flexible method for designing fitness landscapes with specific neutral degree distributions, enabling the study of how neutrality and deceptiveness influence metaheuristic performance.

Contribution

It presents a novel three-step approach to generate customizable neutral fitness landscapes, filling a gap in existing models.

Findings

Able to generate landscapes with targeted neutral degree distributions

Highlights the interaction between deceptiveness and neutrality in optimization

Provides a tool for analyzing metaheuristic performance on neutral landscapes

Abstract

When a considerable number of mutations have no effects on fitness values, the fitness landscape is said neutral. In order to study the interplay between neutrality, which exists in many real-world applications, and performances of metaheuristics, it is useful to design landscapes which make it possible to tune precisely neutral degree distribution. Even though many neutral landscape models have already been designed, none of them are general enough to create landscapes with specific neutral degree distributions. We propose three steps to design such landscapes: first using an algorithm we construct a landscape whose distribution roughly fits the target one, then we use a simulated annealing heuristic to bring closer the two distributions and finally we affect fitness values to each neutral network. Then using this new family of fitness landscapes we are able to highlight the interplay between deceptiveness and neutrality.