Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since theâ€¦ (More)

In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivityâ€¦ (More)

This paper reviews the various studies that have introduced adaptive and self-adaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation isâ€¦ (More)

Abstract. Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adaptâ€¦ (More)

Evolutionary programming and genetic algorithms share many features, not the least of which is a reliance of an analogy to natural selection over a population as a means of implementing search. Withâ€¦ (More)

Angeline 23 Fogel, D. B (1993). Using evolutionary programming to create neural networks that are capable of playing Tic-Tac-Toe. (1992). Evolution as a theme in artificial life: The genesys/trackerâ€¦ (More)

Several evolutionary simulations allow for a dynamic resizing of the genotype. This is an important alternative to constraining the genotypeâ€™s maximum size and complexity. In this paper, we add anâ€¦ (More)

In this paper1 we describe a genetic algorithm capable of evolving large programs by exploiting two new genetic operators which construct and deconstruct parameterized subroutines. These subroutinesâ€¦ (More)