Learning Genetic Algorithms

D.E. Goldberg, Genetic Algorithms in Search, Optimi2ation and Machine Learning, Addison-Wesley, New York, 1989. 

Concomitantly with the increase in hardware capabilities, better software techniques will have to be developed. It will pay us to continue to learn how nature tackles problems. Artificial neural networks are a far cry away from the capabilities of the human brain. There is a lot of room left from the information processing of the human brain in order to develop more powerful artificial neural networks. Nature has developed over millions of years efficient optimization methods for adapting to changes in the environment. The development of evolutionary and genetic algorithms will continue. 

Goldberg D E 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA., Addison-W esley. 

D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading (Mass.), 1989. 2-263 W. Braun, G. Held, H.-P. Steinruck,... 

Goldberg, D.E. (1983) Computer-aided gas pipeline operation using genetic algorithms and rule learning (Doctoral dissertation. University of Michigan). Dissertation Abstracts International, 44(10), p. 3174B (University Microfilms No. 8402282). 

Richards, et. al. s idea is to use a genetic algorithm to search through a space of a certain class of cellular automata rules for a local rule that best reproduces the observed behavior of the data. Their learning algorithm (which was applied specifically to sequential patterns of dendrites formed by NH4 Br as it solidifies from a supersaturated solution) starts with no a-priori knowledge about the physical system. R, instead, builds increasingly sophisticated models that reproduce the observed behavior. 

Richards, et. al. comment that while the exact relationship between the rule found by their genetic algorithm and the fundamental equations of motion for the solidification remains unknown, it may still be possible to connect certain features of the learned rule to phenomenological models. 

B. Carse and T.C. Fogarty, Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm. Lecture Notes in Computer Science, 1143, (1996) 1-22. 

Evolutionary computation which is learned by watching population dynamics the most important programming are genetic algorithms which are inspired by the evolutionary processes of mutation, recombination, and natural selection in biology. 

Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York... 

A helpful starting point for further investigation is Learning Classifier Systems From Foundations to Applications.1 The literature in classifier systems is far thinner than that in genetic algorithms, artificial neural networks, and other methods discussed in this book. A productive way to uncover more... 

Goldberg, D. E. (1989). Genetic algorithms in search, optimit.ation, and machine learning. Reading,... 

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