โ Scribed by John J. Grefenstette (auth.), John J. Grefenstette (eds.)
No coin nor oath required. For personal study only.
The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference.
Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation).
Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm.
The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning.
Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
Front Matter....Pages i-iv
Introduction....Pages 1-4
Using Genetic Algorithms for Concept Learning....Pages 5-32
A Knowledge-Intensive Genetic Algorithm for Supervised Learning....Pages 33-72
Competition-Based Induction of Decision Models from Examples....Pages 73-101
Genetic Reinforcement Learning for Neurocontrol Problems....Pages 103-128
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation....Pages 129-163
Back Matter....Pages 165-165
Artificial Intelligence (incl. Robotics)
๐ SIMILAR VOLUMES
Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This s
Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to ex
This book describes the theory, operation, and application of genetic algorithms-search algorithms based on the mechanics of natural selection and genetics.