Evolving robot behavior via interactive evolutionary computation: from real-world to simulation
Proceedings of the 2001 ACM symposium on Applied computing
Reference chromosome to overcome user fatigue in IEC
New Generation Computing
An input method using discrete fitness values for interactive GA
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
IGAP: interactive genetic algorithm peer to peer
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Towards creative design using collaborative interactive genetic algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Journal of Heuristics
On interactive evolution strategies
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Language generation for conversational agent by evolution of plan trees with genetic programming
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
User-Centric optimization with evolutionary and memetic systems
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
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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.