Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
The theory of evolution strategies
The theory of evolution strategies
On Interactive Evolutionary Algorithms and Stochastic Mealy Automata
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolution Strategies with Subjective Selection
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Human-Computer Interaction (3rd Edition)
Human-Computer Interaction (3rd Edition)
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
On the Role of Temporary Storage in Interactive Evolution
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Tone mapping by interactive evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Bio-inspired combinatorial optimization: notes on reactive and proactive interaction
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
User-Centric optimization with evolutionary and memetic systems
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Optimization of weighted vector directional filters using an interactive evolutionary algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
In this paper we discuss Evolution Strategies within the context of interactive optimization. Different modes of interaction will be classified and compared. A focus will be on the suitability of the approach in cases, where the selection of individuals is done by a human user based on subjective evaluation. We compare the convergence dynamics of different approaches and discuss typical patterns of user interactions observed in empirical studies. The discussion of empirical results will be based on a survey conducted via the world wide web. A color (pattern) redesign problems from literature will be adopted and extended. The simplicity of the chosen problems allowed us to let a larger number of people participate in our study. The amount of data collected makes it possible to add statistical support to our hypothesis about the performance and behavior of different Interactive Evolution Strategies and to figure out high-performing instantiations of the approach. The behavior of the user was also compared to a deterministic selection of the best individual by the computer. This allowed us to figure out how much the convergence speed is affected by noise and to estimate the potential for accelerating the algorithm by means of advanced user interaction schemes.