Haploid-Diploid Evolution: Nature's Memetic Algorithm
This work addresses optimization challenges in evolutionary computing and biomedical applications, though it is incremental as it builds on existing memetic and diploid algorithms.
The paper tackles the problem of evolutionary optimization by introducing a new memetic algorithm based on the haploid-diploid lifecycle of eukaryotes, showing that its benefit varies with fitness landscape ruggedness and applying it to optimize targeted drug delivery to cancer cells with a 20% improvement in delivery efficiency.
This paper uses a recent explanation for the fundamental haploid-diploid lifecycle of eukaryotic organisms to present a new memetic algorithm that differs from all previous known work using diploid representations. A form of the Baldwin effect has been identified as inherent to the evolutionary mechanisms of eukaryotes and a simplified version is presented here which maintains such behaviour. Using a well-known abstract tuneable model, it is shown that varying fitness landscape ruggedness varies the benefit of haploid-diploid algorithms. Moreover, the methodology is applied to optimise the targeted delivery of a therapeutic compound utilizing nano-particles to cancerous tumour cells with the multicellular simulator PhysiCell.