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In this work, the authors show the performance of the proposed diploid scheme (a representation where each individual contains two genotypes) with respect to two dynamic optimization problems, while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms (EAs) to standard EAs. The paper aims to discuss this issue.

In the proposed diploid representation of EA, each individual possesses two copies of the genotype. In order to convert this pair of genotypes to a single phenotype, each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype. In order to provide a fair and objective comparison, the authors make sure to compare populations which contain the same amount of genetic information, where the only difference is the arrangement and interpretation of the information. The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position.

For small fitness landscapes the haploid (standard) and diploid algorithms perform comparably and are able to find the global optimum very quickly. However, as the search space increases, rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum. Since both algorithms use the same amount of genetic information, it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better.

The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele (value) in the vector of values that is the genotype, dominance is adopted across the entire genotype in relation to its homologue. As a result, this representation can be extended across any alphabet, for any optimization function.

Political scientists have taken up behavior genetics (BG) at a momentous time in the science of genetics. Momentous, because the science of genetics is undergoing a paradigm shift [Petronis, A. (2010). Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature, 465(7299), 721–727]. This shifting paradigm poses a significant challenge to both the prevailing methodologies of behavior genetics – twin, family, adoption studies – and one of the most noteworthy findings to emerge from such studies, that is, which we can call the principle of minimal parental effects. This is the supposition that the effect of the shared parental rearing environment on the behavioral phenotypes of offspring is statistically equivalent to zero (Plomin & Daniels, 1987). It is not uncommon nowadays to find twin, adoption, and family studies utilized in the study of political behavior (e.g., Alford, J., Funk, C. L., & Hibbing, J. R. (2005). Are political orientations genetically transmitted? American Political Science Review, 99(2), 153–167.); likewise, the principle of minimal parental effects is frequently invoked in such studies (e.g., Mondak, J. J., Hibbing, M. V., Canache, D., Seligson, M. A., & Anderson, M. A. (2010). Personality and civic engagement: An integrative framework for the study of trait effects on political behavior. American Political Science Review, 104(1), 85–110.). As we shall see, the challenge comes from recent discoveries in genetics that are radically transforming our understanding of the genome and its relationship to environment.