Articles, Books, Consulting
Genetic Algorithms in Search, Optimization and Machine Learning.
Pub. Co.1989. ISBN: 0201157675.
David Goldberg's Genetic Algorithms in Search, Optimization
and Machine Learning is by far the bestselling introduction
to genetic algorithms. Goldberg is one of the preeminent researchers
in the field--he has published over 100 research articles on
genetic algorithms and is a student of John Holland, the father
of genetic algorithms--and his deep understanding of the material
shines through. The book contains a complete listing of a simple
genetic algorithm in Pascal, which C programmers can easily
understand. The book covers all of the important topics in the
field, including crossover, mutation, classifier systems, and
fitness scaling, giving a novice with a computer science background
enough information to implement a genetic algorithm and describe
genetic algorithms to a friend.
Goldberg, David E.
Skills and Leadership for Engineers. McGraw Hill
College Div. 1995. ISBN:0070236895.
Covering the practical skills necessary for an engineer
to prosper in the professional field of engineering, this
book reviews the importance of writing, leadership, developing
presentation skills, organization, ethics, meeting participation
and technical skills. Real life experiences amongst engineering
colleagues are discussed with total emphasis on achieving
life time success in a career of engineering.
and Accurate Parallel Genetic Algorithms (Genetic Algorithms and
Evolutionary Computation Volume 1). 2000. Kluwer Academic
Pub; ISBN: 0792372212
genetic algorithms (GAs) become increasingly popular, they are
applied to difficult problems that may require considerable
computations. In such cases, parallel implementations of GAs
become necessary to reach high-quality solutions in reasonable
times. But, even though their mechanics are simple, parallel
GAs are complex non-linear algorithms that are controlled by
many parameters, which are not well understood. Efficient and
Accurate Parallel Genetic Algorithms is about the design of
parallel GAs. It presents theoretical developments that improve
our understanding of the effect of the algorithm's parameters
on its search for quality and efficiency. These developments
are used to formulate guidelines on how to choose the parameter
values that minimize the execution time while consistently reaching
solutions of high quality. Efficient and Accurate Parallel Genetic
Algorithms can be read in several ways, depending on the readers'
interests and their previous knowledge about these algorithms.
Newcomers to the field will find the background material in
each chapter useful to become acquainted with previous work,
and to understand the problems that must be faced to design
efficient and reliable algorithms. Potential users of parallel
GAs that may have doubts about their practicality or reliability
may be more confident after reading this book and understanding
the algorithms better. Those who are ready to try a parallel
GA on their applications may choose to skim through the background
material, and use the results directly without following the
derivations in detail. These readers will find that using the
results can help them to choose the type of parallel GA that
best suits their needs, without having to invest the time to
implement and test various options. Once that is settled, even
the most experienced users dread the long and frustrating experience
of configuring their algorithms by trial and error. The guidelines
contained herein will shorten dramatically the time spent tweaking
the algorithm, although some experimentation may still be needed
for fine-tuning. Efficient and Accurate Parallel Genetic Algorithms
is suitable as a secondary text for a graduate level course,
and as a reference for researchers and practitioners in industry.
Whitley, David Goldberg, Erick Cantu-Paz, Lee Spector, Ian Parmee,
& Hans-Georg Beyer(editors)
2000: Proceedings of the Genetic and Evolutionary Computation
Academic Press. ISBN: 1558607080
2000 Genetic and Evolutionary Computation Conference (GECCO-00)
combines the longest running conference in evolutionary computation
(ICGA) and the world's two largest EC conferences (GP and ICGA)
to create a unique opportunity to bring together the best in
research in the growing field of genetic and evolutionary computation
The GECCO conference continues the tradition of the GP and ICGA
conferences of bringing together researchers from the entire
spectrum of research in evolutionary computation, including
genetic algorithms, classifier systems, genetic programming,
evolvable hardware, DNA and molecular computing, evolutionary
strategies, evolutionary programming, artificial life, adaptive
behavior, agents, as well as real-world applications of all
of these areas.
and Evolutionary Computation . Kluwer
International Series on Genetic Algorithms and Evolutionary
and practitioners alike are increasingly turning to search,
optimization, and machine-learning procedures based on natural
selection and genetics to solve problems across the spectrum
of human endeavor. These genetic algorithms and techniques of
evolutionary computation are solving problems and inventing
new hardware and software that rival human designs. Genetic
Algorithms and Evolutionary Computation will publish research
monographs, edited collections, and graduate-level texts in
this rapidly growing field. Primary areas of coverage include
the theory, implementation, and application of genetic algorithms
(GAs), evolution strategies (ESs), evolutionary programming
(EP), learning classifier systems (LCSs) and other variants
of genetic and evolutionary computation (GEC). Proposals in
related fields such as artificial life, adaptive behavior, artificial
immune systems, agent-based systems, neural computing, fuzzy
systems, and quantum computing will be considered for publication
in this series as long as GEC techniques are part of or inspiration
for the system being described. Manuscripts describing GEC applications
in all areas of engineering, commerce, the sciences, and the
humanities are encouraged.
Koza, John R., David E. Goldberg and David B. Fogel (Eds.).
Programming 1996 : Proceedings of the First Annual Conference,
July 28-31, 1996, Stanford University (Complex Adaptive Systems).
MIT. Hardcover. 1996. ISBN: 0262611279.
University Genetic programming is a domain-independent method
for automatic programming that evolves computer programs that
solve, or approximately solve, problems. Starting with a primordial
ooze of thousands of randomly created computer programs composed
of functions and terminals appropriate to a problem, a population
of programs is progressively evolved over many generations using
the Darwinian principle of survival of the fittest, a sexual
recombination operation, and occasional mutation. These proceedings
of the first Genetic Programming Conference present the most
recent research in the field of genetic programming as well
as recent research results in the fields of genetic algorithms,
evolutionary programming, and learning classifier systems. Topics
include: Applications of genetic programming. Theoretical foundations
of genetic programming. Implementation issues. Technique extensions.
Automated synthesis of analog electrical circuits. Automatic
programming of cellular automata. Induction. System identification.
Control. Evolution of machine language programs. Automatic programming
of multi-agent strategies. Automated evolution of program architecture.
Evolution of mental models. Implementations of memory and state.
Cellular encoding. Evolvable hardware. Parallelization techniques.
Relations to biology and cognitive systems. Genetic algorithms.
Evolutionary programming. Evolution strategies. Learning classifier
systems. Complex Adaptive Systems series. A Bradford Book .
John, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco
Dorigo, David Fogel, Max Garzon, David Goldberg, Hitoshi Iba,
Rick Riolo (Eds.).
Programming 1998. Academic Press. Morgan Kaufmann. Paperback.
1998. ISBN: 1558605487.
of the Annual Conferences on Genetic Programming. These proceedings
present the most recent research in the field of genetic programming
as well as recent research results in the fields of genetic
algorithms, artificial life and evolution strategies, DNA
computing, evolvable hardware, and genetic learning classifier
Books, Consulting Editor