IEEE International Conference on Evolutionary Computation 

About: IEEE International Conference on Evolutionary Computation is an academic conference. The conference publishes majorly in the area(s): Genetic algorithm & Evolutionary computation. Over the lifetime, 1039 publications have been published by the conference receiving 45147 citations.

A modified particle swarm optimizer

Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

The particle swarm: social adaptation of knowledge

Abstract: Particle swarm adaptation is an optimization paradigm that simulates the ability of human societies to process knowledge The algorithm models the exploration of a problem space by a population of individuals; individuals' successes influence their searches and those of their peers The algorithm is relevant to cognition, in particular the representation of schematic knowledge in neural networks Particle swarm optimization successfully optimizes network weights, simulating the adaptive sharing of representations among social collaborators The paper introduces the algorithm, begins to develop a social science context for it, and explores some aspects of its functioning

Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation

Abstract: A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean is presented. This enables the evolution strategy (ES) to adapt the correct scaling of a given problem and also ensures invariance with respect to any rotation of the fitness function (or the coordinate system). Especially rotation invariance, here resulting directly from the coordinate system independent adaptation of the mutation distribution, is an essential feature of the ES with regard to its general applicability to complex fitness functions. Compared to previous work on this subject, the introduced formulation facilitates an interpretation of the resulting mutation distribution, making sensible manipulation by the user possible (if desired). Furthermore it enables a more effective control of the overall mutation variance (expected step length).

Using selection to improve particle swarm optimization

Abstract: This paper describes a evolutionary optimization algorithm that is a hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations. A comparison is performed between the hybrid swarm and the ordinary particle swarm that shows selection to provide an advantage for some (but not all) complex functions.

MAX-MIN Ant System and local search for the traveling salesman problem

Abstract: Ant System is a general purpose algorithm inspired by the study of the behavior of ant colonies. It is based on a cooperative search paradigm that is applicable to the solution of combinatorial optimization problems. We introduce MAX-MIN Ant System, an improved version of basic Ant System, and report our results for its application to symmetric and asymmetric instances of the well known traveling salesman problem. We show how MAX-MIN Ant System can be significantly improved, extending it with local search heuristics. Our results clearly show that MAX-MIN Ant System has the property of effectively guiding the local search heuristics towards promising regions of the search space by generating good initial tours.