Finding a Mate With No Social Skills

Sexual reproductive behavior has a necessary social coordination component as willing and capable partners must both be in the right place at the right time. While there are many known social behavioral adaptations to support solutions to this problem, we explore the possibility and likelihood of solutions that rely only on non-social mechanisms. We find three kinds of social organization that help solve this social coordination problem (herding, assortative mating, and natal philopatry) emerge in populations of simulated agents with no social mechanisms available to support these organizations. We conclude that the non-social origins of these social organizations around sexual reproduction may provide the environment for the development of social solutions to the same and different problems.Comment: 8 pages, 5 figures, GECCO'1

Evidence of assortative mating in autism spectrum disorder

Background Assortative mating is a non-random mating system in which individuals with similar genotypes and/or phenotypes mate with one another more frequently than would be expected in a random mating system. Assortative mating has been hypothesized to play a role in Autism Spectrum Disorder (ASD) in an attempt to explain some of the increase in the prevalence of ASD that has recently been observed. ASD is considered to be a heritable neurodevelopmental disorder but there is limited understanding of its causes. Assortative mating can be explored through both phenotypic and genotypic data, but up until now, has never been investigated through genotypic measures in ASD. Methods We investigated genotypically similar mating pairs using genome-wide Single Nucleotide Polymorphism (SNP) data on trio families (Autism Genome Project (AGP) data (1,590 parents) and Simons Simplex Collection (SSC) data (1962 parents)). To determine whether or not an excess in genetic similarity was present we employed kinship coefficients and examined spousal correlation between the principal components in both the AGP and SSC datasets. We also examined assortative mating using phenotype data on the parents to detect any correlation between ASD traits. Results We found significant evidence of genetic similarity between the parents of ASD offspring using both methods in the AGP dataset. In the SSC, there was also significant evidence of genetic similarity between the parents when explored through spousal correlation. Conclusions This gives further support to the hypothesis that positive assortative mating plays a role in ASD

Genotypic and phenotypic assortative mating in genetic algorithm

Three new methods of selection of mating pairs for Genetic Algorithms (GAs) are introduced where the partners are chosen based on either their genotypic similarity (called genotypic assortative mating) or their phenotypic similarity (called phenotypic assortative mating). These methods not only help in exploiting the current search space properly before exploring the new one but also enable one to mimic inbreeding of natural genetics. A comparative study in terms of disruption of schema due to crossover is made between these methods and conventional genetic algorithm (CGA). The superiority of this new methodology over the CGA and the incest prevention algorithm is established on some problems of optimizing complex functions and selecting optimal neura

A study of evolutionary multiobjective algorithms and their application to knapsack and nurse scheduling problems

Evolutionary algorithms (EAs) based on the concept of Pareto dominance seem the most suitable technique for multiobjective optimisation. In multiobjective optimisation, several criteria (usually conflicting) need to be taken into consideration simultaneously to assess a quality of a solution. Instead of finding a single solution, a set of trade-off or compromise solutions that represents a good approximation to the Pareto optimal set is often required. This thesis presents an investigation on evolutionary algorithms within the framework of multiobjective optimisation. This addresses a number of key issues in evolutionary multiobjective optimisation. Also, a new evolutionary multiobjective (EMO) algorithm is proposed. Firstly, this new EMO algorithm is applied to solve the multiple 0/1 knapsack problem (a wellknown benchmark multiobjective combinatorial optimisation problem) producing competitive results when compared to other state-of-the-art MOEAs. Secondly, this thesis also investigates the application of general EMO algorithms to solve real-world nurse scheduling problems. One of the challenges in solving real-world nurse scheduling problems is that these problems are highly constrained and specific-problem heuristics are normally required to handle these constraints. These heuristics have considerable influence on the search which could override the effect that general EMO algorithms could have in the solution process when applied to this type of problems. This thesis outlines a proposal for a general approach to model the nurse scheduling problems without the requirement of problem-specific heuristics so that general EMO algorithms could be applied. This would also help to assess the problems and the performance of general EMO algorithms more fairly

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field