When using an evolutionary algorithm to solve a problem involving building blocks we have to grow the building blocks and then mix these building blocks to obtain the (optimal) solution. Finding a good balance between the growing and the mixing process is a prerequisite to get a reliable evolutionary algorithm. Different building blocks can have different probabilities of being mixed. Such differences can easily lead to a loss of the building blocks that are difficult to mix and as a result to premature convergence. By allocating relatively many trials to individuals that contain building blocks with a low mixing probability we can prevent such effects. We developed the mixing evolutionary algorithm (mixEA) in which the allocation of trials is a more explicit procedure than in the standard evolutionary algorithms. Experiments indicate that the mixEA is a reliable optimizer on a set of building block problems that are difficult to handle with more traditional genetic algorithms. In the case that the global optimum is not found, the mixEA creates a small population containing a high concentration of building blocks.

Optimization (acm G.1.6), Problem Solving, Control Methods, and Search (acm I.2.8)
Learning and adaptive systems (msc 68T05), Problem solving (heuristics, search strategies, etc.) (msc 68T20)
CWI
Software Engineering [SEN]

van Kemenade, C.H.M. (1997). The mixing evolutionary algorithm : indepedent selection and allocation of trials. Software Engineering [SEN]. CWI.