The Evolutionary Buffet Method

Within the field of Genetic Algorithms (GA) and Artificial Intelligence (AI) a variety computational substrates with the power to find solutions to a large variety of problems have been described. Research has specialized on different computational substrates that each excel in different problem domains. For example, Artificial Neural Networks (ANN) (Russell et al., Artificial intelligence: a modern approach, vol 2. Prentice Hall, Upper Saddle River, 2003) have proven effective at classification, Genetic Programs (by which we mean mathematical tree-based genetic programming and will abbreviate with GP) (Koza, Stat Comput 4:87–112, 1994) are often used to find complex equations to fit data, Neuro Evolution of Augmenting Topologies (NEAT) (Stanley and Miikkulainen, Evolut Comput 10:99–127, 2002) is good at robotics control problems (Cully et al., Nature 521:503, 2015), and Markov Brains (MB) (Edlund et al., PLoS Comput Biol 7:e1002,236, 2011; Marstaller et al., Neural Comput 25:2079–2107, 2013; Hintze et al., Markov brains: a technical introduction. arXiv:1709.05601, 2017) are used to test hypotheses about evolutionary behavior (Olson et al., J R Soc Interf 10:20130,305, 2013) (among many other examples). Given the wide range of problems and vast number of computational substrates practitioners of GA and AI face the difficulty that every new problem requires an assessment to find an appropriate computational substrates and specific parameter tuning to achieve optimal results.