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2 Evolution of Learning in Neural Networks2.1 General FrameworkThe main idea in using genetic connectionism to model the evolution of learning is simple:use a genome to encode not the static properties of a network, but the dynamic properties.Specifically, the genome encodes a procedure for changing the connection strengths of a networkover time, based on past performance and environmental information. In the experiment to beoutlined here, the genome encodes no static properties at all. The initial configuration of anetwork is instead determined randomly (within a constrained framework). Thus, if geneticsearch is to be successful, it must evolve a learning procedure that can start from a wide varietyof network configurations and reliably reach an appropriate result.Working within this paradigm it is important to distinguish two levels of adaptation: learning— adaptation at the level of the individual — where an individual over time adapts to itsenvironment, and evolution — adaptation at the level of the population — where over the courseof evolutionary history a population gradually evolves mechanisms that improve the fitness of itsmembers. In our case, the fitness of a single member of the population will be measured by itscapacity for adaptation within its lifetime (learning); so the evolutionary mechanisms can beviewed as a second-order adaptive process: a population-wide adaptation that over generationsproduces better adaptive processes within the lifetime of an individual.A vital component of the learning process is the environment. If the environment wererelatively static, there might be little need for learning to evolve. Systems could instead evolveto a state where they have innate mechanisms to handle that environment. But if theenvironment is diverse and unpredictable, innate environment-specific mechanisms are of littleuse. Instead, individuals need general adaptive mechanisms to cope with arbitrary environments.In this way, a diverse environment encourages the evolution of learning.Our basic modus operandi is as follows. A genome encodes the weight-space dynamics of aconnectionist system — that is, it encodes a potential learning procedure. The fitness of alearning procedure is determined by applying it to a number of different learnable tasks. This isachieved by creating a number of networks and putting them in different environments for aspecified amount of time. (In the experiments to be outlined here, an environment consists of aset of input patterns with associated training signals.) Each network has a random initial state,but its final state is determined by its interaction with the learning procedure and the5
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