Bio-inspired Combinatorial Optimization: Notes on Reactive and Proactive Interaction

Evolutionary combinatorial optimization (ECO) is a branch of evolutionary computing (EC) focused on finding optimal values for combinatorial problems. Algorithms ranging in this category require that the user defines, before the process of evolution, the fitness measure (i.e., the evaluation function) that will be used to guide the evolution of candidate solutions. However, there are many problems that possess aesthetical or psychological features and as a consequence fitness evaluation functions are difficult, or even impossible, to formulate mathematically. Interactive evolutionary computation (IEC) has recently been proposed as a part of EC to cope with this problem and its classical version basically consists of incorporating human user evaluation during the evolutionary procedure. This is however not the only way that the user can influence the evolution in IEC and currently one can find that IEC has been been successfully deployed on a number of hard combinatorial optimization problems. This work examines the application of IEC to these problems. We describe the basic fundament of IEC, present some guidelines to the design of interactive evolutionary algorithms (IEAs) to handle combinatorial optimization problems, and discuss the two main models over which IEC is constructed, namely reactive and proactive searchbased schemas. An overview of the existing literature on the topic is also provided. We conclude with some reflections on the lessons learned, and the future directions that research might take in this area.