Optimal Classifier System Performance in Non-Markov Environments
Lanzi, P.L., Wilson, S.W. (1999)TR No.: 99022 | Download PDF | Download PS
Abstract:
Wilson’s (1994) bit-register memory scheme was incorporated into the XCS classifier system and investigated in a series of non-Markov environments. Two extensions to the scheme proved important for reaching optimal performance in the harder environments. The first was an exploration strategy in which exploration of external actions was probabilistic as in Markov environments, but internal […]
Posted: April 20th, 1999 under Genetic algorithms. Comments: none
An Implementation of the XCS classifier system in C
Butz, M. (1999)TR No.: 99021 | Download PDF | Download PS
Abstract:
The XCS classifier system was developed by Wilson (1995). The learning mechanism is based on the accuracy of its reward prediction. This method leads to the formation of accurate most general classifiers. This paper explains how to download, compile and use the XCS code version 1.0 written in ANSI C. It discusses how to select […]
Posted: April 16th, 1999 under Genetic algorithms. Comments: none
Technical Writing for Fun & Profit
Goldberg, D.E. (1999)TR No.: 99020 | Download PDF | Download PS
Abstract:The average engineering student would rather go to the dentist and have root canal than write a technical report or a memo. This is unfortunate, as a large part of a working engineer’s professional life is spent in writing technical communiqués of one sort or another. Although, the widespread aversion to writing has a variety of […]
Posted: April 12th, 1999 under Genetic algorithms. Comments: none
New Challenges for an Anticipatory Classifier System: Hard Problems and Possible Solutions
Butz, M., Goldberg, D.E., Stolzmann, W. (1999)TR No.: 99019 | Download PDF | Download PS
Abstract:
An Anticipatory Classifier System (ACS) is a learning mechanism based on learning classifier systems and the cognitive model of “Anticipatory Behavioral Control”.
By comparing perceived consequences with its own expectations (anticipations), an ACS is able to learn in multi-step environments. To date, the ACS has proven its abilities in various problems of that kind. It is […]
Posted: April 8th, 1999 under Genetic algorithms. Comments: none
A Survey of Optimization by Building and Using Probabilistic Models.
Pelikan, M., Goldberg, D.E., Lobo, F. (1999)TR No.: 99018 | Download PDF | Download PS
Abstract:
This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated. All methods are classified […]
Posted: April 4th, 1999 under Genetic algorithms. Comments: none
Designing Efficient and Accurate Parallel Genetic Algorithms.
Cantu-Paz, E. (1999)TR No.: 99017 | Download PDF | Download PS
Abstract:
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions.
However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insufficient knowledge limits our ability to design fast and […]
Posted: March 24th, 1999 under Genetic algorithms. Comments: none
Extended Compact Genetic Algorithm in C++
Lobo, F. and Harik, G. (1999)TR No.: 99016 | Download PDF | Download PS
Abstract:
This report tells you how to download, compile, and run the extended compact genetic algorithm (ECGA) described in Harik’s paper (Harik, 1999). It also explains how to modify the objective function that comes with the distribution of the code. The source is written in C++ but a knowledge of the C programming language is sufficient […]
Posted: March 20th, 1999 under Genetic algorithms. Comments: none
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Cantu-Paz, E. (1999)TR No.: 99015 | Download PDF | Download PS
Abstract:
This paper investigates how the policy used to select migrants and replacements affects the selection pressure in parallel evolutionary algorithms (EAs) with multiple populations. The four possible combinations of random and fitness-based emigration and replacement of existing individuals are considered. The investigation follows two approaches. The first is to calculate the takeover time under the […]
Posted: March 16th, 1999 under Genetic algorithms. Comments: none
Parameter-less Genetic Algorithm: A Worst-case Time and Space Complexity Analysis
Pelikan, M. and Lobo, F. (1999)TR No.: 99014 | Download PDF | Download PS
Abstract:
In this paper, the worst-case analysis of the time and space complexity of the parameter-less genetic algorithm versus the genetic algorithm with an optimal population size is provided and the results of the analysis are discussed. Since the assumptions in order for the analysis to be correct are very weak, the result is applicable to […]
Posted: March 12th, 1999 under Genetic algorithms. Comments: none
Genetic and Evolutionary Algorithms in the Real World
Goldberg, D.E. (1999)TR No.: 99013 | Download PDF | Download PS
Abstract:
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Posted: March 8th, 1999 under Genetic algorithms. Comments: none