Goldberg, David E.
Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co.1989. ISBN: 0201157675.

Reviews from Amazon.com:
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.
Life Skills and Leadership for Engineers. McGraw Hill College Div. 1995. ISBN:0070236895.

From The Publisher:
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.

As 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.

Darrell Whitley, David Goldberg, Erick Cantu-Paz, Lee Spector, Ian Parmee, & Hans-Georg Beyer(editors)
GECCO 2000: Proceedings of the Genetic and Evolutionary Computation Conference. 2000. Academic Press. ISBN: 1558607080

The 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 (GEC).

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.

Goldberg, David E.
Genetic Algorithms and Evolutionary Computation . Kluwer International Series on Genetic Algorithms and Evolutionary Computation.

Researchers 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.).
Genetic Programming 1996 : Proceedings of the First Annual Conference, July 28-31, 1996, Stanford University (Complex Adaptive Systems). MIT. Hardcover. 1996. ISBN: 0262611279.

Stanford 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 .

Proceedings 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 systems.

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