Evolutionary computation: comments on the history and current state

Abstract

Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming (GP) and classifier systems (CS)], evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

Optimización basada en Colonia de Hormigas aplicada al problema de Planificación de Celdas en redes de radio para sistemas de telefonía móvil

“…Entre los modelos más estudiados se encuentran las Metaheurísticas Evolutivas (Bäck, Hammel, & Schwefel, 1997) (Cano, Herrera, & Lozano, 2003), las cuales utilizan el principio de la evolución de las especies para explorar espacios de búsqueda complejos. En (Luna Valero, 2008) se pueden encontrar las propuestas de optimización evolutiva más relevantes para este problema.…”

Receiver alignment dependence of a GA controlled optical wireless transmitter

“…GAs should be considered as a general framework that needs to be tailored to a specific problem [28]. Our initial work [13], detailed justified the methodology used to adapt the representation, fitness function, selection, recombination and mutation sub-routines found in the so-called canonical GA.…”

“…Mutation was originally developed as a background operator [28], able to introduce new genetic material into the search routine such that the probability of evaluating a solution in Φ g will never be zero. Mutation is performed on each individual scaling factor, a i ∈ a ν ∀ν ∈ {1, .…”

Semi-automatic Catheter Reconstruction from Two Views

“…Additionally, the size of the approximated ellipse is used to reject candidates for which the ellipse is not plausible with respect to the known catheter shape. Since the search space for the ellipse search is very large, a population based search strategy is used [1]. In this strategy, an initial population of short paths starting from the seedpoint is evaluated.…”