This paper presents a personal account of the author's 35 years “adventure” with Evolutionary Computation—from the first encounter in 1988 and many years of academic research through to working full-time in business—successfully implementing evolutionary algorithms for some of the world's largest corporations. The paper concludes with some observations and insights.

This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids. In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n -dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

In this paper we present the concept of evolution programs and discuss a hierarchy of such programs for a particular problem. We argue that (for a particular problem) stronger evolution programs (in terms of the problem-specific knowledge incorporated in the system) should perform better than weaker ones. This hypothesis is based on a number of experiments and a simple intuition that problem-specific knowledge enhances an algorithm's performance; at the same time it narrows the applicability of an algorithm. Trade-offs between the effort of finding an effective representation for general-purpose evolution programs and the effort of developing more specialized systems are also discussed.