Genetic Redundancy: Desirable or Problematic for Evolutionary Adaptation?
Evolution is commonly viewed as a process of hill climbing on a fitness landscape. A major problem with such a view is the presence of local optima; sub-optimal regions of the landscape from which no further progress is possible. There is an increasing amount of evidence [3,4,7], however, that the presence of large degrees of redundancy in the genome may alleviate this problem through the creation of neutral networks; sets of genotypes at the same level of fitness that are connected by single point mutations. These networks allow drift at the same fitness level and hence may increase the reliability of the evolutionary process by allowing the exploration of larger portions of genotype space. The presence, or otherwise, of genetic redundancy could thus be an important concern in the design of artificial evolutionary systems. This paper explores the effects of genetic redundancy in the context of an evolutionary robotics experiment. Neural network control systems are evolved for a simple navigation task and the speed and reliability of the evolutionary process ascertained for differing levels of redundancy. Evolutionary progress is found to halt far more readily as the degree of redundancy is reduced indicating a greater probability of entrapment at local optima.
KeywordsField Programmable Gate Array Fitness Landscape Hill Climbing Neutral Network Genetic Redundancy
Unable to display preview. Download preview PDF.
- Huynen, M.: Exploring phenotype Space through neutral evolution. Santa Fe Institute Working Paper, 95-10-100, 1995.Google Scholar
- Gruner, W., Giegerich, R., Strothmann, D., Reidys, C., Weber, J., Hofacker, I., Stadler, P., Schuster, P.: Analysis of RNA Sequence Structure Maps by Exhaustive Enumeration. Part One: Neutral Networks. University of Vienna, Theoretical Biochemistry Group Working Paper, 95-10-16, 1995.Google Scholar
- Gruner, W., Giegerich, R., Strothmann, D., Reidys, C., Weber, J., Hofacker, L, Stadler, P., Schuster, P.: Analysis of RNA Sequence Structure Maps by Exhaustive Enumeration. Part Two: Structures of Neutral Networks and Shape Space Covering. University of Vienna, Theoretical Biochemistry Group Working Paper, 95-10-16, 1995.Google Scholar
- van Nimwegen, E., Crutchfield, J.: Optimizing Epochal Evolutionary Search: Population-Size Independent Theory. Santa Fe Institute Working Paper, 98-06-046, 1998.Google Scholar
- Hoshino, T., Tsuchida, M.: Manifestation of Neutral Genes in Evolving Robot Navigation. Proc. of Artifical Life V, pp. 408–415, 1996.Google Scholar
- Harvey, I., Thompson, A.: Through the Labyrinth Evolution Finds a Way: A Silicon Ridge. Proc. of the First International Conference on Evolvable Systems: From Biology to Hardware (ICES96). Berlin: Springer Verlag, 1996.Google Scholar
- Barnett, L.: Tangled Webs: Evolutionary Dynamics on Fitness Landscapes with Neutrality. University of Sussex, MSc Dissertation, 1997.Google Scholar
- Newman, M., Engelhardt, R.: Effects of Neutral Selection on the Evolution of Molecular Species. Santa Fe Institute Working Paper, 98-01-001,1998.Google Scholar
- Jakobi, N.: Evolutionary Robotics and the Radical Envelope of Noise Hypothesis. University of Sussex, Cognitive Science Research Paper 457, 1997.Google Scholar
- Collins, R., Jefferson, D.: Selection in Massively Parallel Genetic Algorithms. Proc. of the Fourth International Conference of Genetic Algorithms (ICGA-91), pp. 249–256, Morgan Kauffman, 1991.Google Scholar
- Ohta, T.: Population Size and rate of evolution. Journal of Molecular Evolution, vol. 1, 1972.Google Scholar
- Jakobi, N.: Encoding Scheme Issues for Open-Ended Artificial Evolution. Proc. of Parallel Processing in Nature, pp. 52–61, Springer-Verlag, 1996.Google Scholar