Comparing genetic robustness in generational vs. steady state evolutionary algorithms

Abstract

Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust solutions as wellsolutions where average loss of fitness due to crossover and other genetic variation operators is minimized. It has been shown that in a simple "two peaks" problem, where the fitness landscape consists of a broad, low peak, and a narrow, high peak, individuals initially converge on the lower (less fit), but broader peak, and that increasing an individual's genetic robustness through growth is a necessary prerequisite for convergence on the higher, narrower peak [18]. If growth is restricted, the population remains converged on the less fit solution. We tested whether this result holds true only for generational algorithms, or whether it applies to steady state algorithms as well. We conclude that although growth occurs with both algorithms, the steady state algorithm is able to converge on the higher peak without this growth. This result shows that the role of genetic robustness in the evolutionary process is significantly different in generational versus steady state algorithms.

Multithreaded computing in evolutionary design and in artificial life simulations

“…All tests reported in this work were performed using the standard-mt experiment definition-a multithreaded version of the most common and versatile Framsticks evolutionary optimization experiment [24]. This experiment script performs physical simulation of creatures built from genotypes that are mutated and crossed over in the course of a steady-state (i.e., non-generational) evolution [16,26,38]. The most computationally expensive part of the optimization process is the evaluation of fitness of each genotype.…”

Critical mutation rate has an exponential dependence on population size

“…Comas et al used population sizes as low as 250 and concluded "that the critical mutation rate was independent of population size" (Comas et al, 2005) despite the fact that there did appear to be some correlation for certain cases. Jones and Soule (2006) determined that the role of genetic robustness in evolution differs significantly depending on whether it is a generational or steady state genetic algorithm that is being used. In a steady state algorithm, only a few individuals are replaced at a time, as opposed to a generational algorithm which replaces the entire population at once.…”

“…In a steady state algorithm, only a few individuals are replaced at a time, as opposed to a generational algorithm which replaces the entire population at once. Many studies that have confirmed the notion of survival-of-the-flattest have used generational models, such as Wilke et al's (2001) evolution of digital organisms in Avida, and Krakauer and Plotkin's (2002) study of redundancy and antiredundancy (Jones and Soule, 2006). Jones and Soule suggest that for evolutionary dynamics experiments, the class of algorithm used can have a significant effect on the observed outcome.…”

“…In any evolutionary system, including genetic algorithms and natural biological systems, there is significant evolutionary pressure to evolve sequences that are both fit and robust (Jones and Soule, 2006). Robustness is defined as the average effect of a specified perturbation (such as a new mutation) on the fitness of a specified genotype (Masel and Trotter, 2010).…”

Critical Mutation Rate Has an Exponential Dependence on Population Size

“…Population dynamics can be modelled in silico using genetic algorithms, in which populations of sequences are allowed to undergo mutation, recombination and selection at specified rates; studies can be done in a controlled environment within time-frames not possible in many natural biological systems, producing results that are comparable both to theory and to experimental results in microorganisms. In any evolutionary system, including genetic algorithms and natural biological systems, there is significant evolutionary pressure to evolve sequences that are both fit and robust (Jones and Soule, 2006). Robustness is defined as the average effect of a specified perturbation (such as a new mutation) on the fitness of a specified genotype (Masel and Trotter, 2010).…”