In a marker-based encoding of a neural network, each neuron definition consists of a collection of connections specified between a start and an end marker in the chromosome. This mechanism allows all aspects of the network structure, including the number of nodes and their connectivity, to be evolved through genetic algorithms. The search is free to utilize material between neuron definitions, which allows for drastic exploration of solutions space. The method has been shown efficient in learning finite state behavior in an artificial environment and learning strategies for the game of Othello.