Adaptation in Natural and Artificial Systems
Адаптация в естественных и искусственных системах
1992-04-29
SCID: 54.1/2ya4vprr
Discuss with AI
coadaptation and coevolutioncomplex adaptive systemsgenetic algorithmsmathematical geneticsschemata
Figures from the paper
Abstract (AI)
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements. Bradford Books imprint
Key Findings
1
Holland introduces a mathematical model capturing nonlinear interactions in adaptive systems.
2
The approach explains coadaptation and coevolution through the emergence, recombination, and inheritance of building blocks called schemata.
3
The book establishes genetic algorithms as a framework for studying adaptation in complex natural and artificial systems.
4
The framework’s universality is illustrated through applications to economics, physiological psychology, game theory, and artificial intelligence.
5
The work extends analysis from simple artificial systems to complex naturally occurring processes involving multiple interacting factors and revises traditional perspectives in mathematical genetics.
Research Object
complex adaptive systems in natural and artificial domains
Research Subject
mathematical modeling of nonlinear adaptation, coadaptation, and coevolution, including the emergence and recombination of schemata
Publication Details
Publication Date
1992-04-29
Journal
Publisher
ISSN
Cited by
35497
Access Type
Author Information
Download PDF
Subscribe to digest
Cited by8
Data clustering1999
Reinforcement Learning: A Survey1996
Review of Deep Learning Algorithms and Architectures2019
The application of intelligent hybrid techniques for the mass appraisal of residential properties1999
Connectionist Model to Estimate Performance of Steam-Assisted Gravity Drainage in Fractured and Unfractured Petroleum Reservoirs: Enhanced Oil Recovery Implications2013
Air traffic simulation in chemistry-climate model EMAC 2.41: AirTraf 1.02016
CMS Physics Technical Design Report, Volume II: Physics Performance2007
PERSPECTIVE: COMPLEX ADAPTATIONS AND THE EVOLUTION OF EVOLVABILITY1996