A methodology to perform generalized zeroth-order two- and three-dimensional shape optimization utilizing a learning classifier system is presented and applied. Specifically, the methodology has the objective of determining the optimal boundary to ...

We propose fast evolutionary programming (FEP) for optimizing the parameters of a reduced parameter bilinear model (RPBL) used for predicting nonlinear and chaotic time series. The RPBL model is capable of effectively representing nonlinearity and ...

Evolutionary programming is a stochastic optimization algorithm that can be used for system identification. This paper focuses on the use of evolutionary programming for optimizing models of chaotic signals, both with and without additive noise. ...

We show that for variable rates of mutation and crossover that depend on the global fitness of the population, and selection of reproduction pair that is asymmetric, solutions to problems with stringent constraints are found.

This paper describes a genetic algorithm approach to the real-time optimization of a class of Local Area Network (LAN) topologies under measured traffic patterns. This approach consists of two parts: 1) a genetic algorithm approach to optimizing LAN ...

This research applies techniques and tools from Genetic Programming (GP) to the facility layout problem. The facility layout problem (FLP) is an NP-complete combinatorial optimization problem that has applications to efficient facility design for ...