‘‘Whatever Works Best for You’’- A New Method for a Priori and Progressive Multi-objective Optimisation
作者： Rui Wang , Robin C. Purshouse , Peter J. Fleming
DOI: 10.1007/978-3-642-37140-0_27
关键词: A priori and a posteriori 、 Decision maker 、 Preference (economics) 、 Machine learning 、 Space (commercial competition) 、 Artificial intelligence 、 Computer science 、 Set (abstract data type) 、 Process (engineering) 、 Evolutionary algorithm 、 Benchmark (computing)
摘要: Various multi-objective evolutionary algorithms (MOEAs) have been developed to help a decision maker (DM) search for his/her preferred solutions problems. However, none of these approaches has catered simultaneously the two fundamental ways that DM can specify preferences: weights and aspiration levels. In this paper, we propose an approach named iPICEA-g allows his preference in either format. is based on preference-inspired co-evolutionary algorithm (PICEA-g). Solutions are guided toward regions interest (ROIs) by co-evolving sets goal vectors exclusively generated ROIs. Moreover, friendly making technique interaction with optimization process: specifies preferences easily interactively brushing objective space. No direct elicitation numbers required, reducing cognitive burden DM. The performance tested set benchmark problems shown be good.
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