Evolutionary Computation Meets Machine Learning: A Survey
Abstract
Evolutionary computation (EC) [1] is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms (EA) [2] is frequently treated the same as EC. Generally speaking, EC algorithms include genetic algorithm (GA), evolutionary programming (EP), evolutionary strategies (ES), genetic programming (GP), learning classifier systems (LCS), differential evolution (DE), and estimation of distribution algorithm (EDA). Recently, swarm intelligence (SI) [3] algorithms like ant colony optimization (ACO) and particle swarm optimization (PSO) have also been proposed as optimization methodologies and have gained increasing popularity in the EC research community. SI algorithms share many common characteristics with EAs and are also regarded to be in the EC algorithm family [4]. In this article, we regard EC algorithms to include both EAs and SI algorithms.
- Publication:
-
IEEE Computational Intelligence Magazine
- Pub Date:
- 2011
- DOI:
- Bibcode:
- 2011ICIM....6d..68Z
- Keywords:
-
- Evolutionary computation;
- Machine learning