Deceptive fitness landscapes are a growing concern for evolutionary computation. Recent
work has shown that combining human insights with short-term evolution has a synergistic …

Interactive decision making methods use preference information from the decision maker
during the optimization task to guide the search towards favourite solutions. In real-life …

Since their inception, active interactive genetic algorithms have successfully combat user
evaluation fatigue induced by repetitive evaluation. Their success originates on building …

Interactive Evolutionary Computation (IEC) has been applied to art and design problems
where the fitness of an individual is at least partially subjective. Applications usually present …

The necessary intervention of humans in interactive evolutionary computational systems has
inherent drawbacks arising from the very nature of the algorithms, namely the human fatigue …

In the early phase of a design process, conceptualization and preliminary refinement are
important processes in deciding the quality of a product; however, their implementation in …

Interactive genetic algorithms are effective methods of solving optimization problems with
implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional …

Interactive Evolutionary Computation (IEC) is a kind of human–machine interaction
calculation method derived from evolutionary computation. The main problem of interactive …

We present an analysis of user fatigue mitigation techniques in interactive genetic
algorithms (IGAs). A user guides the evolutionary process by picking every t generations the …

Making products fit the feelings and preferences of many users is one of the targets of
Kansei engineering. At present, it is still challenging to achieve the target. Interactive …