One of the most popular evolutionary algorithms is genetic algorithm.
Our new version performs better than the most popular evolutionary algorithms and it is comparable to very recent state-of-the-art metaheuristics, like Multi-objective Memetic Algorithm based on Decomposition (MOMAD), for multi-objective programming.
Furthermore, the proposed algorithm has shown a superior performance in finding the near-optimum solution for this task when it is compared to the most popular evolutionary algorithms in engineering applications, i.e. genetic algorithm (GA) and particle swarm optimization (PSO).
Finally, we discuss why the two most popular evolutionary theories of aging, mutation accumulation and antagonistic pleiotropy, may underestimate the effect of selection on survival at old ages.
Evidence for systematic error when estimating divergence affects many evolutionary analyses where relative branch length estimates are important, such as molecular dating or studying adaptation, but the most popular evolutionary analysis remains inferring a phylogenetic tree.
In this paper, we introduce MatrixConverter, a program that helps expedite and facilitate the transformation of raw phenomic character data into discrete character matrices that can be used in most popular evolutionary inference programs.
