Evolving solutions: The genetic algorithm and evolution strategies for finding optimal parameters

This chapter provides an introduction to evolutionary algorithms (EAs) and their applicability to various biological problems. There is a focus on EAs' use as an optimization technique for fitting parameters to a model. A number of design issues are discussed including the data structure being operated on (chromosome), the construction of robust fitness functions, and intuitive breeding strategies. Two detailed biological examples are given. The first example demonstrates the EA's ability to optimize parameters of various ion channel conductances in a model neuron by using a fitness function that incorporates the dynamic range of the data. The second example shows how the EA can be used in a hybrid technique with classification algorithms for more accuracy in the classifier, feature pruning, and for obtaining relevant combinations of features. This hybrid technique allows researchers to glean an understanding of important features and relationships embedded in their data that might otherwise remain hidden.