On the Workings of Genetic Algorithms: The Genoclique Fixing Hypothesis

TL;DR

This paper introduces the genoclique fixing hypothesis to explain how simple genetic algorithms efficiently adapt to complex fitness functions, supported by empirical results showing performance improvements on MAX 3-SAT problems.

Contribution

It proposes a new hypothesis about genetic algorithm functioning, surpassing the building block hypothesis, and demonstrates its validity through empirical experiments.

Findings

Clamping significantly improved SGA performance on MAX 3-SAT.

SGA can perform sublinear computation related to attribute interactions.

The proposed hypothesis offers a better explanation than the building block hypothesis.

Abstract

We recently reported that the simple genetic algorithm (SGA) is capable of performing a remarkable form of sublinear computation which has a straightforward connection with the general problem of interacting attributes in data-mining. In this paper we explain how the SGA can leverage this computational proficiency to perform efficient adaptation on a broad class of fitness functions. Based on the relative ease with which a practical fitness function might belong to this broad class, we submit a new hypothesis about the workings of genetic algorithms. We explain why our hypothesis is superior to the building block hypothesis, and, by way of empirical validation, we present the results of an experiment in which the use of a simple mechanism called clamping dramatically improved the performance of an SGA with uniform crossover on large, randomly generated instances of the MAX 3-SAT problem.