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.

Recognizing and reconstructing simple polygons from their combinatorial visibility graphs remains one of the open problems in computational geometry. Although much work has been completed on restricted versions of the problem, very little is known about ...

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 ...

Inductive Logic Programming (ILP) and Genetic Programming (GP) are two approaches in program induction. However, they are restricted in the computer languages in which programs can be induced. Generic Genetic Programming (GGP) is a novel, powerful, and ...

We present a genetic programming (GP) technique to find cliques in a graph [Haynes and Schoenefeld, 1996]. A collection of cliques in a graph can be represented as a list of a list of nodes which, in turn, can be represented by a tree structure. Given a ...

For Genetic Programming to be accepted by the mainstream computer science community, an area which must be addressed is that of reducing the time taken to arrive at solutions. Fairly extensive work has been carried out on the parallelisation of genetic ...

The Paragen system is a new technique for the automatic conversion of sequential programs into functionally equivalent parallel programs. This technique utilizes GP to generate a highly parallel program from an original sequential program, while ...

Database query optimization is a hard research problem. Exhaustive techniques are adequate for trivial instances only, while combinatorial optimization techniques are vulnerable to the peculiarities of specific instances. We propose a model based on ...

One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic ...

The use of bias with automated learning systems has become an important area of research. The use of bias with evolutionary techniques of learning has been shown to have advantages when complex structures are evolved. This is especially true when the ...

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 ...

Specification refinement is part of formal program derivation, a method by which software is directly constructed from a provably correct specification. Because program derivation is an intensive manual exercise used for critical software systems, an ...

Compiling Genetic Programming Systems ('CPGS') are advanced evolutionary algorithms that directly evolve RISC machine code. In this paper we compare the ability of CGPS to generalize with that of other machine learning ('ML') paradigms.

This study ...

This paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify actions for the incremental growth of a deterministic finite automata from an ...

It is difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances in the cellular space. Various human-written algorithms have ...