Evolutionary Computation

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Abstract

Evolutionary computation is a method of solving engineering problems using algorithms that mimic Darwinian natural selection and Mendelian genetics, applied especially to optimization problems that are difficult to solve from first principles. Earliest beginnings were in the 1950s, and by the mid-1990s it had developed as an academic field with its own journals, conferences, and faculty. Several phenomena discovered in evolutionary biology were also discovered in parallel in evolutionary computation, including the evolvability problem, genetic modification, constructive neutral evolution, and genetic robustness. The related field of artificial life focuses on computational systems in which replication, natural selection, and ecological interactions are all emergent.

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Lee Altenberg is a Senior Fellow at the Konrad Lorenz Institute for Evolution and Cognition Research. As an undergraduate at the University of California, Berkeley, he was interested in complex systems phenomena in nature, and decided that evolutionary theory would provide a rich field for discovery. He studied theoretical population genetics with Glenys Thomson, and studied with Marcus W. Feldman at Stanford University for his doctorate, introducing his advisor to the work of John H. Holland. In his postdoctoral work at Stanford and Duke University, he developed early mechanistic models of evolution that shaped the genotype–phenotype map in the direction of modularity and the evolution of evolvability. He introduced an analytical framework for the evolution of the distribution of fitness effects and evolvability from population genetics into the field of evolutionary computation. He served as associate professor in the Department of Information and Computer Sciences at the University of Hawai`i at Manoa. He has recently been focused on obtaining new results in spectral theory to answer questions of complex information transmission in evolution.

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