Evolutionary Programming 

About: Evolutionary Programming is an academic journal. The journal publishes majorly in the area(s): Evolutionary programming & Evolutionary algorithm. Over the lifetime, 207 publications have been published receiving 11841 citations.

Parameter Selection in Particle Swarm Optimization

Abstract: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters. Analysis of experiments demonstrates the validity of these guidelines.

Comparison between Genetic Algorithms and Particle Swarm Optimization

Abstract: This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.

Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences

Abstract: This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization. The method of processing employed in each technique are first reviewed followed by a summary of their philosophical differences. Comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the techniques.

Fast Evolution Strategies

Abstract: Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming

A Survey of Constraint Handling Techniques in Evolutionary Computation Methods.

Abstract: One of the major components of any evolutionary system is the evaluation function. Evaluation functions are used to assign a quality measure for individuals in a population. Whereas evolutionary computation techniques assume the existence of an (e cient) evaluation function for feasible individuals, there is no uniform methodology for handling (i.e., evaluating) unfeasible ones. The simplest approach, incorporated by evolution strategies and a version of evolutionary programming (for numerical optimization problems), is to reject unfeasible solutions. But several other methods for handling unfeasible individuals have emerged recently. This paper reviews such methods (using a domain of nonlinear programming problems) and discusses their merits and drawbacks.