Genetic algorithms with implicit memory

This thesis investigates the workings of genetic algorithms in dynamic optimisation problems where fitness landscapes materialise that are identical to, or resemble in some way, landscapes previously encountered. The objective is to appraise the performances of the various approaches offered by the GAs. Approaches specifically tailored for different kinds of dynamic environment lie outside the remit of the thesis. The main topics that are explored are: genetic redundancy, modularity, neutral evolution, explicit memory, and implicit memory. It is in the matter of implicit memory that the thesis makes the majority of its novel contributions. It is demonstrated via experimental analysis that the pre-existing techniques are deficient, and a new algorithm – the pointer genetic algorithm (pGA) – is expounded and assessed in an attempt to offer an improvement. It is shown that though it outperforms its rivals, it cannot attain the performance levels of an explicit memory algorithm (that is, an algorithm using an external memory bank). The main claims of the thesis are that with regard to memory, the pre-existing implicit-memory algorithms are deficient, the new pointer GA is superior, and that because all of the implicit approaches are inferior to explicit approaches, it is explicit approaches that should be used in real-world problem solving

Initial Modelling of the Alternative Phenotypes Hypothesis

The alternative phenotype hypothesis contends that multiple phenotypes exist in a single genotype and are expressed by environmental or genetic cues. It further states that these multiple phenotypes will be maintained and improved in a population where the environment is unstable, in spite of the increased cost of this plasticity. In this work we propose a simple computational model to investigate the conditions under which alternative phenotypes become beneficial, and persist over evolutionary timescales. We find that the environment must vary to realise this hypothesis, and that these adaptations not only provide a fitness benefit in highly unstable environments but also continue to arise despite increasing stability and a corresponding gradual decline in fitness