An empirical investigation of how and why neutrality affects evolutionary search

Abstract

The effects of neutrality on evolutionary search have been considered in a number of interesting studies, the results of which, however, have been contradictory. Some researchers have found neutrality to be beneficial to aid evolution whereas others have argued that the presence of neutrality in the evolutionary process is useless. We believe that this confusion is due to several reasons: many studies have based their conclusions on performance statistics (e.g., on whether or not a system with neutrality could solve a particular problem faster than a system without neutrality) rather than a more in-depth analysis of population dynamics, studies often consider problems, representations and search algorithms that are relatively complex and so results represent the compositions of multiple effects (e.g., bloat or spurious attractors in genetic programming), there is not a single definition of neutrality and different studies have added neutrality to problems in radically different ways. In this paper, we try to shed some light on neutrality by addressing these problems. That is, we use the simplest possible definition of neutrality (a neutral network of constant fitness, identically distributed in the whole search space), we consider one of the simplest possible algorithms (a mutation based, binary genetic algorithm) applied to two simple problems (a unimodal landscape and a deceptive landscape), and analyse both performance figures and, critically, population flows from and to the neutral network and the basins of attraction of the optima.

Robustness, Evolvability, and Accessibility in Linear Genetic Programming

“…Using a simple set of experiments, recent analysis has demonstrated this benefit to be problem dependent [9]. Our results provide a more subtle picture.…”

“…While some studies have found no benefit [2,3,4], others have claimed that neutrality provides a buffer against deleterious genetic perturbation [5,6] and reduces the risk of premature convergence through an expansion of the search space [7,8]. As argued in [9], the lack of consensus regarding the benefits of neutrality largely stem from the overly complex problems, representations, and search algorithms used in these analyses, which make it difficult to tease apart the effects of neutrality from other confounding factors. Further, neutrality is often artificially added to the problem representation and little attention is paid to how this alters the fitness landscape [9].…”

“…As argued in [9], the lack of consensus regarding the benefits of neutrality largely stem from the overly complex problems, representations, and search algorithms used in these analyses, which make it difficult to tease apart the effects of neutrality from other confounding factors. Further, neutrality is often artificially added to the problem representation and little attention is paid to how this alters the fitness landscape [9].…”

Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity

“…Despite its proven success, it also suffers from some limitations and researchers have been interested in making GP more robust, or reliable, by studying various elements of the search process (e.g., neutrality [4], [8], [9], [21], locality [5], [6], [7], special representations [3]). …”

Searching for novel regression functions

“…Moreover, this is also true for evolutionary algorithms (EAs), where the search for better fitness guides the evolutionary process. Fitness is the core element in understanding EA dynamics, with a large amount of research devoted at describing, understanding and analyzing fitness functions [1] and fitness landscapes [2], [3]; such as work done on locality [4], [5] and neutrality [6], [7], [8]. Fitness-based algorithms have achieved impressive results in many domains, for instance just considering the genetic programming (GP) paradigm a long list of examples exist [9].…”