Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9)

Boston, Massachusetts

September 12-15th 2004

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Artwork by Dr. Cliff Pickover used by permission





Alife 9 Tutorial

Life as it is? Population genetics basics for evolutionary computation experts.

Richard A. Watson and Daniel M. Weinreich
Organismic and Evolutionary Biology, Harvard University.

Synopsis: A tour of basic evolutionary genetics for evolutionary computation practitioners.

We provide a description of some of the basic models and assumptions used in evolutionary genetics and population genetics as practiced in evolutionary biology. We contrast these models and assumptions, appropriate for life as it is, with the more liberal models and assumptions common in evolutionary computation and artificial life. There are, of course, many technical details of genetics and natural populations that are often overlooked in computational abstractions: e.g. mutation rates, genome structure, and selection coefficients. But rather than focus on these details, we will focus more on the impact that such details have had on how the research questions and models of the two disciplines differ. For example: Population genetics models sometimes disregard new mutations and account only for changes in frequency of existing alleles; Many models that do account for new mutations focus on deleterious mutations, and/or neutral mutations, and less emphasis is placed on beneficial mutations; Population genetics models also tend to address a small number of loci and often ignore epistasis or use simplified assumptions about epistasis. These types of models do not merely reflect the different styles of different disciplines but are motivated by biological observations and theoretical concerns such as the maintenance of variation under blending inheritance, and the inability of natural populations to select for sets of alleles. By better understanding the empirical and philosophical grounding of models used in evolutionary biology we can better understand how to facilitate communication and cross-fertilization between, on the one hand, artificial life and evolutionary computation, and on the other, the evolutionary biology that inspired it.

Part I: A population genetics primer

  • Evolutionary biology historical perspectives:

  • Natural selection, the Modern Synthesis, Mass action, Neutral theory

  • Basic questions focusing on dynamics of alleles:

  • Probability of fixation of an allele; time to fixation of an allele

Part II: Epistasis and fitness landscapes

  • Definitions of epistasis and landscapes:

  • Synergistic, statistical, and unidimensional epistasis; allele-frequency vs. genotype spaces

  • Contrasting assumptions in biology and computer science

  • Magnitude vs. sign epistasis; assumption of single-peaked landscapes

Part III: Sexual recombination

  • Simple models for the benefit of sex in population genetics:

  • Fisher/Muller; Deterministic Mutation Hypothesis

  • Selection on combinations of alleles vs. individual alleles

  • Shifting Balance Theory; The role of sex in disrupting vs. creating combinations of alleles