We propose a decomposition-based interactive evolutionary algorithm (EA) for multiple
objective optimization. During an evolutionary search, a decision maker (DM) is asked to …

We propose a novel co-evolutionary algorithm for interactive multiple objective optimization,
named CIEMO/D. It aims at finding a region in the Pareto front that is highly relevant to the …

We introduce a family of interactive evolutionary algorithms for Multiple Objective
Optimization (MOO). In the phase of preference elicitation, a Decision Maker (DM) is asked …

Evolutionary multi-objective optimization (EMO) methodologies have been widely applied to
find a well-distributed trade-off solutions approximating to the Pareto-optimal front in the past …

We consider essential challenges related to the elicitation of indirect preference information
in interactive evolutionary algorithms for multiple objective optimization. The methods in this …

This paper describes a new Preference-based Interactive Evolutionary (PIE) algorithm for
multi-objective optimization which exploits the advantages of both evolutionary algorithms …

This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that
attempts to learn a value function capturing the users' true preferences. At regular intervals …

Over the years, scalarization functions have been used to solve multiobjective optimization
problems by converting them to one or more single objective optimization problem (s). This …

Evolutionary multiobjective optimization (EMO) methodologies have gained popularity in
finding a representative set of Pareto optimal solutions in the past decade and beyond …

We develop a preference-based multiobjective evolutionary algorithm that interacts with the
decision maker (DM) during the course of optimization. We create a territory around each …