A note on using genetic and evolutionary algorithms for multi-periodcommunication network optimisation
Rothlauf, F., Grasser, C. (2000)TR No.: 2000029 | Download PDF | Download PS
Abstract:
This paper addresses the optimization of telecommunication networks for
a multi-period horizon. Four heuristics are presented to cope with the
problem to minimize the overall costs for the network over several
periods. For the minimization of cost we use a simple genetic algorithm
(GA). It is adapted in different ways to the special structure of the
network problem.
Even in […]
Posted: March 4th, 2000 under Genetic algorithms. Comments: none
Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm
Knjazew, D., Goldberg, D.E (2000)TR No.: 2000012 | Download PDF | Download PS
Abstract:
This paper presents a scaling analysis of the ordering messy genetic algorithm (OmeGA), a fast messy genetic algorithm that uses random keys to represent solutions.
In experiments with hard permutation problems - so-called ordering deceptive problems - it is shown that the algorithm scales up as O(l^1.4) with the problem length l ranging from 32 to […]
Posted: March 4th, 2000 under Genetic algorithms. Comments: none
The parameter-less genetic algorithm: Rational and automated parameter selection for simplified genetic algorithm operation
Fernando Lobo (2000)TR No.: 2000030 | Download PDF | Download PS
Abstract:
Genetic algorithms (GAs) have been used to solve difficult optimization
problems in a number of fields. One of the advantages of these
algorithms is that they operate well even in domains where little is
known, thus giving the GA the flavor
of a general purpose problem solver.
However, in order to solve a problem with the GA, the user usually
has […]
Posted: February 24th, 2000 under Genetic algorithms. Comments: none
Pruefernumbers and Genetic Algorithms: A lesson how the low locality of an encoding can harm the performance of GAs
Rothlauf, F., Goldberg, D.E. (2000)TR No.: 2000011 | Download PDF | Download PS
Abstract:
When handling tree networks, researchers have sometimes tried using the pruefernumber representation for encoding networks, but GAs often degraded or broke down when used on this encoding. This paper investigates the locality of the pruefernumber and its effect on the performance of a Genetic Algorithm (GA). The locality describes how the neighborhood of the genotype […]
Posted: February 24th, 2000 under Genetic algorithms. Comments: none
You Know You’re an Excellent Senior Design Team If You…
Goldberg, D.E. (2000)TR No.: 2000021 | Download PDF | Download PS
Abstract:
Related PostsNo related posts
Posted: February 20th, 2000 under Genetic algorithms. Comments: none
Research on the Bayesian Optimization Algorithm
Pelikan, M., Goldberg, D.E. (2000)TR No.: 2000010 | Download PDF | Download PS
Abstract:
This paper summarizes our recent research on the Bayesian optimization algorithm (BOA) and outlines the directions our research in this area has been following. It settles the algorithm in the problem decomposition framework used often to understand the complex behavior of genetic algorithms. It provides the most important research issues to tackle and reviews our […]
Posted: February 20th, 2000 under Genetic algorithms. Comments: none
XCSJava 1.0: An implementation of the XCS classifier system in Java
Butz, M.V. (2000)TR No.: 2000027 | Download PDF | Download PS
Abstract:
The XCSJava 1.0 implementation of the XCS classifier system in Java is
freely available from the IlliGAL anonymous ftp-site. The implementation
covers the basic features of the XCS classifier system and provides
a multiplexer and maze environment for testing purposes. This paper
explains how to download, compile, and run the code. Moreover, it explains
the object oriented approach in the […]
Posted: February 16th, 2000 under Genetic algorithms. Comments: none
Some Reflections on Learning Classifier Systems
Goldberg, D.E. (2000)TR No.: 2000009 | Download PDF | Download PS
Abstract:
I appreciate the editors’ invitation to contribute to this important volume marking what must be called a renaissance of learning classifier systems (LCSs). Although I have kept my finger in the LCS pie through occasional papers on LCS subjects, the main body of my work shifted following my 1983 dissertation applying genetic algorithms (GAs) and […]
Posted: February 16th, 2000 under Genetic algorithms. Comments: none
On Extended Compact Genetic Algorithm
Sastry, K., Goldberg, D.E. (2000)TR No.: 2000026 | Download PDF | Download PS
Abstract:
In this study we present a detailed analysis of the extended compact
genetic algorithm (ECGA). Based on the analysis, empirical relations for
population sizing and convergence time have been derived and are compared
with the existing relations. We then apply ECGA to a non-azeotropic binary
working fluid power cycle optimization problem. The optimal power cycle
obtained improved the cycle efficiency […]
Posted: February 12th, 2000 under Genetic algorithms. Comments: none
First Cognitive Capabilities in the Anticipatory Classifier System
Stolzmann, W., Butz, M.V., Hoffman, J., Goldberg, D.E. (2000)TR No.: 2000008 | Download PDF | Download PS
Abstract:
This paper adds a new viewpoint to the Anticipatory Classifier System (ACS). It approaches the system from a psychological perspective and thus provides new insights to the current system. The main learning mechanism in the ACS, the Anticipatory Learning Process (ALP), evolved out of the psychological learning theory of anticipatory behavioral control. The paper compares […]
Posted: February 12th, 2000 under Genetic algorithms. Comments: none