2009/05/15 by Keki M. Burjorjee, Burjorjee, Keki M.
Computer Science · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.NE
paper · pdf · doi:10.48550/arxiv.0905.2473
25 pages, 7 figures
arxiv created 2009/05/15 · openalex publication_date 2009/05/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.