An evolutionary classifier, such as a learning classifier system (LCS) or a genetic programming boolean concept learner, must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain "cooperative ...

Learning classifier systems (LCSs) have existed for nearly twenty years (Holland & Reitman, 1978). Research efforts in reinforcement learning (RL), evolutionary computation (EC), and neural networks have enhanced the original LCS paradigm. New thoughts ...

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 ...

The problem of unsupervised learning of patterns in spatial data is addressed using evolutionary programming. Hyperbox clusters of two-dimensional data are evolved in light of a minimum description length (MDL) principle. A series of experiments of ...

Extracting a license plate is an important stage in the automatic car identification. It is very difficult because car images are usually degraded and processing the images is computationally intensive.

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 ...

We propose a method for evolving strategies based on the nearest-neighbor rule. A strategy corresponds to an action selection function which chooses an action depending on the current state of a (reactive) control problem to be solved. Given a set I of ...

A Genetic Algorithm (GA) is applied to derive circuits that combine area efficiency and testability. These circuits are obtained from Ordered Kronecker Functional Decision Diagrams (OKFDDs). In "Becker and Drechsler (1995)" a heuristic approach has been ...

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 ...

A major strength of GAs is the ability to search large problem spaces, given a suitable fitness function. The feasibility of exploiting this strength of GAs to find software errors was explored by focusing on software APIs and commands that have ...

In recent years, evolutionary algorithms (EAs) have been successfully used in the design of artificial neural networks for a variety of applications. The suitability of EAs for this design task stems from their ability to adaptively search large spaces ...

We study a class of genetic algorithms (GA) based on a population of chromosomes with mutation and crossover, as well as fitness. The chromosomes in these algorithms also have another property we call "activity" (which determines how fast a chromosome ...

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 ...

This system explores the union between the hip joint and a prosthesis inserted in the femur. The objective is to make an early detection of the existence of any gapping between prosthesis and bone. To do this, we use an ANN built with a GA, that ...

A new approach to how to evolve integer recurrences using Genetic Programming (GP) is taken where the recurrences fix their base cases in accordance with the set of test cases instead of evolving them. A number of experiments indicate that this approach ...

Ayala & Kiger (1984) document a biological phenomenon in which co-adapted genes migrate and naturally cluster together into structures called supergenes. We have begun research into the implications of the supergene effect on the efficiency of ...

A genetic programming (GP) system is presented that mirrors the conceptual structure of the genetic programming cycle, maximising flexibility and re-use of code. This reduces the effort required to apply GP to a new problem domain.

Genetic programming (GP) is used to classify human brain tumours based on ¹H Magnetic Resonance spectra. Good classification was achieved by GP (compared to a neural network). GP classification used simple combinations of variables, corresponding to a ...

This paper describes a method for inverse kinematics which uses genetic programming. This method is able to automate low-level motion control, yet still give animators control over the visual qualities of their work. The main advantage of this method is ...