Randomized search heuristics like simulated annealing and evolutionary algorithms are
applied successfully in many different situations. However, the theory on these algorithms is …

We present a study on the effects of non-random mating and varying population size in
genetic algorithm (GA) performance. We tested two algorithms: the non-incest genetic …

Accelerating a genetic algorithm (GA) by implementing it in a reconfigurable field
programmable gate array (FPGA) is described. The implemented GA features: random …

This paper addresses the issue of what makes a problem genetic programming (GP)-hard
by considering the binomial-3 problem. In the process, we discuss the efficacy of the …

A genetic algorithm is a biologically inspired method for function optimization that is loosely
based on the theory of evolution. It is typically used when there is little knowledge of the …

Evolutionary algorithms(EAs) are population-based search heuristics often used for function
optimization. Typically they employ selection, crossover, and mutation as search operators. It …

This thesis is (mostly) about making maps. Specifically, it is concerned with the problem of
automating the placement of names on them. Maps are a very convenient means of …

In this paper, we consider the problem of finding good next moves in two-player games.
Traditional search algorithms, such as minimax and α-β pruning, suffer great temporal and …

In this chapter, we present a survival-based, steady-state GA designed for efficient
implementation in hardware and the design of a pipelined genetic algorithm processor that …

This paper addresses the problem of how and when learning is an aid to evolutionary
search in hierarchical modular tasks. It brings together two major areas of research in …