The Coevolution of Mutation Rates

In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of genes for longevity and mutation rate in the individuals. This made it possible for a lineage to evolve to be immortal. It also allowed the evolution of no mutation or extremely high mutation rates. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that different genes may have evolved different mutation rates as adaptations to the varying pressures of interactions with other genes.

[1]  A. Colman.  Game theory and experimental games , 1982 .

[2]  Manfred Milinski,et al.  Cooperation wins and stays , 1993, Nature.

[3]  G. Wilkinson.  Reciprocal food sharing in the vampire bat , 1984, Nature.

[4]  Biosciences Colloquium,et al.  Analysis of ecological systems , 1980 .

[5]  M. Nowak,et al.  A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game , 1993, Nature.

[6]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[7]  R. Boyd,et al.  No pure strategy is evolutionarily stable in the repeated Prisoner's Dilemma game , 1987, Nature.

[8]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[9]  Alice E. Smith,et al.  Expected Allele Coverage and the Role of Mutation in Genetic Algorithms , 1993, ICGA.

[10]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[11]  V. Rich.  Personal communication , 1989, Nature.

[12]  Thomas Bäck,et al.  The Interaction of Mutation Rate, Selection, and Self-Adaptation Within a Genetic Algorithm , 1992, PPSN.

[13]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[14]  J. W. Valentine,et al.  Diversity trends within a model taxonomic hierarchy , 1986 .

[15]  Elliott Sober,et al.  Conceptual issues in evolutionary biology , 1984 .

[16]  Thomas S. Ray,et al.  An Approach to the Synthesis of Life , 1991 .

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  David Jefferson,et al.  Artificial Life as a Tool for Biological Inquiry , 1993, Artificial Life.

[19]  Charles E. Taylor,et al.  Artificial Life II , 1991 .

[20]  John Maynard Smith,et al.  Byte-sized evolution , 1992, Nature.

[21]  Stephanie Forrest,et al.  An Introduction to SFI Echo , 1993 .

[22]  Schloss Birlinghoven,et al.  How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .

[23]  D. DeAngelis,et al.  New Computer Models Unify Ecological TheoryComputer simulations show that many ecological patterns can be explained by interactions among individual organisms , 1988 .

[24]  K. Lindgren,et al.  Cooperation and community structure in artificial ecosystems , 1993 .

[25]  David R. Jefferson,et al.  Artificial Life as a Tool for Biological Inquiry, in Artificial Life: an Overview , 1993 .

[26]  Carlo C. Maley,et al.  Implementing i-state configuration models for population dynamics: an object-oriented programming approach , 1993 .

[27]  D. DeAngelis,et al.  Individual-Based Models and Approaches in Ecology , 1992 .

[28]  J. M. Smith,et al.  Optimization Theory in Evolution , 1978 .

[29]  Tracy M. Sonneborn,et al.  The Origin, Evolution, Nature, and Causes of Aging , 1978 .

[30]  Christopher G. Langton,et al.  Cooperation and Community Structure in Artificial Ecosystems , 1997 .

[31]  C. Maley,et al.  A Model of the E ects of Dispersal Distance on theEvolution of Virulence in ParasitesC , 1994 .

[32]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[33]  R. Cutler,et al.  Evolutionary Biology of Senescence , 1978 .

[34]  Mark F. Bramlette.  Initialization, Mutation and Selection Methods in Genetic Algorithms for Function Optimization , 1991, ICGA.

[35]  R. Boyd.  Mistakes allow evolutionary stability in the repeated prisoner's dilemma game. , 1989, Journal of theoretical biology.

[36]  S. Kauffman,et al.  Coevolution to the edge of chaos: coupled fitness landscapes, poised states, and coevolutionary avalanches. , 1991, Journal of theoretical biology.

[37]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[38]  Graham Bell,et al.  Evolutionary and Nonevolutionary Theories of Senescence , 1984, The American Naturalist.

[39]  Paulien Hogeweg,et al.  Spiral wave structure in pre-biotic evolution: hypercycles stable against parasites , 1991 .

[40]  Vern L. Bengtson,et al.  Emergent Theories of Aging , 1988 .

[41]  M. Nowak,et al.  Tit for tat in heterogeneous populations , 1992, Nature.

[42]  Terence C. Fogarty,et al.  Varying the Probability of Mutation in the Genetic Algorithm , 1989, ICGA.

[43]  Thomas Bäck,et al.  Optimal Mutation Rates in Genetic Search , 1993, ICGA.

[44]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[45]  Reinhard Männer,et al.  Towards an Optimal Mutation Probability for Genetic Algorithms , 1990, PPSN.

[46]  L. Partridge,et al.  Optimally, mutation and the evolution of ageing , 1993, Nature.