: This paper describes a new methodology for using genetic programming to solve the missile countermeasures optimization problem. The resulting system evolves programs that combine...
Abstract. In Neo-Darwinism, mutation can be considered to be unaffected by selection pressure. This is the metaphor generally used by the genetic algorithm for its treatment of the...
This paper describes a GA for job shop scheduling problems. Using the Giffler and Thompson algorithm, we created two new operators, THX crossover and mutation, which better trans...
Shyh-Chang Lin, Erik D. Goodman, William F. Punch ...
Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest t...
: This paper demonstrates that a design for a low-distortion high-gain 96 decibel (64,860 -to-1) operational amplifier (including both circuit topology and component sizing) can be...
John R. Koza, David Andre, Forrest H. Bennett III,...
In spirit of the earlier works done by Arthur (1992) and Palmer et al. (1993), this paper models speculators with genetic programming (GP) in a production economy (Muthian Economy)...
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-...
We use the genetic programming (GP) paradigm for two tasks. The first task given a GP is the generation of rules for the target / clutter classification of a set of synthetic apert...
This paper discusses a simple representation of variable-dimensional optimization problems for evolutionary algorithms. Although it was successfully applied to the optimization of ...
The task of finding minimal elements of a partially ordered set is a generalization of the task of finding the global minimum of a real-valued function or of finding Pareto-optimal...