Ensembled Crossover based Evolutionary Algorithm for Single and Multi-objective Optimization

Shreya Sharma, Julian Blank, Kalyanmoy Deb, Bijaya Ketan Panigrahi

Abstract:

A unique way evolutionary algorithms (EAs) are
different from other search and optimization methods is their
recombination operator. For real-parameter problems, it takes
two or more high-performing population members and blends
them to create one or more new solutions. Many real-parameter
recombination operators have been proposed in the literature.
Each operator involves at least a parameter that controls the
extent of exploration (diversity) of the generated offspring
population. It has been observed that different recombination
operators and specific parameters produce the best performance
for different problems. This fact imposes the user to use
different operator and parameter combinations for every new
problem. While an automated algorithm configuration method
can be applied to find the best combination, in this paper, we
propose an Ensembled Crossover based Evolutionary Algorithm
(EnXEA), which considers a number of recombination operators
simultaneously. Their parameter values and applies them with
a probability updated adaptively in proportion to their success
in creating better offspring solutions. Results on single-objective
and multi-objective, constrained, and unconstrained problems
indicate that EnXEA’s performance is close to the best individual
recombination operation for each problem. This alleviates the use
of expensive parameter tuning either adaptively or manually for
solving a new problem.