Efficiency of crossover in genetic algorithms

Question

I\'ve implemented a number of genetic algorithms to solve a variety of a problems. However I\'m still skeptical of the usefulness of crossover/recombination.

I usually f

Answer 1

My impression is that hill-climbing from multiple random starts is very effective, but that trying to find a case where cross-over can improve on this is non-trivial. One reference is "Crossover: The Divine Afflatus in Search" by David Icl˘anzan, which states

The traditional GA theory is pillared on the Building Block Hypothesis (BBH) which states that Genetic Algorithms (GAs) work by discovering, emphasizing and recombining low order schemata in high-quality strings, in a strongly parallel manner. Historically, attempts to capture the topological fitness landscape features which exemplify this intuitively straight-forward process, have been mostly unsuccessful. Population-based recombinative methods had been repeatedly outperformed on the special designed abstract test suites, by different variants of mutation-based algorithms.

A related paper is "Overcoming Hierarchical Difficulty by Hill-Climbing the Building Block Structure" by David Iclănzan and Dan Dumitrescu, which states

The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are well-suited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from sub-solution with strong non-linear interdependencies. The paper proposes a hill-climber operating over the building block (BB) space that can efficiently address hierarchical problems.