Evolutionary Computing Chapter 14 Chapter 14 Interactive Evolutionary

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Evolutionary Computing Chapter 14

Evolutionary Computing Chapter 14

Chapter 14: Interactive Evolutionary Algorithms • • Motivation Characteristics Approaches Interactive evolution as design

Chapter 14: Interactive Evolutionary Algorithms • • Motivation Characteristics Approaches Interactive evolution as design vs. optimisation 1 /6

Motivation • Interactive evolution (IE): measure of a solution’s fitness is provided by a

Motivation • Interactive evolution (IE): measure of a solution’s fitness is provided by a human’s subjective judgement • World is full of examples of humanoid intervention (pets, food crops) • Applications of IE Algorithms: capturing aesthetics in art and design, personalisation of artefacts such as medical devices • Human’s judgement – Advantage: insight and guidance – Disadvantage: inconsistent, loss of attention 2 /6

Characteristics • The user becomes effectively part of the system (like in agricultural breeding)

Characteristics • The user becomes effectively part of the system (like in agricultural breeding) • Features that impact on the design of IEAs: – Effect of time: • Avoid lengthy evolution and focus on making rapid gains to fit in with human needs • Human decision takes longer than evaluation mathematical fitness function – Effect of context: • Human expectations change in response to what evolution produces – Advantages of IEAs: • Handling situations with no clear fitness function • Improved search ability, increased exploration and diversity 3 /6

Algorithmic Approaches (1/2) Interactive selection and population size • Subjective selection – Direct (choosing

Algorithmic Approaches (1/2) Interactive selection and population size • Subjective selection – Direct (choosing individuals for reproduction) – Indirect (assigning fitness, sorting) • Use of small population because: – Limited number of solutions can be shown – When ranking, the pair-wise comparisons grow rapidly • Multi-objective EAs are used for problems with mixture of quantitative and qualitative aspects 4 /6

Algorithmic Approaches (2/2) Intervention in the variation process • Implicit: periodically adjust the choice

Algorithmic Approaches (2/2) Intervention in the variation process • Implicit: periodically adjust the choice and parameterisation of variation operators, using the given score to control mutation • Explicit: inspect promising solutions to adjust them by hand place them back in to the population (Lamarkian) Use of surrogate fitness function • Approximate the decision a human would make • Advantage: can use large populations 5 /6

Interactive evolution as design vs. optimisation • IE is related to evolutionary art and

Interactive evolution as design vs. optimisation • IE is related to evolutionary art and design 6 /6