期刊： EVOLUTIONARY COMPUTATION, 2021; 29 (2)
Decomposition-based evolutionary algorithms have been quite successful in dealing with multiobjective optimization problems. Recently, more and more r......
期刊： EVOLUTIONARY COMPUTATION, 2021; 29 (2)
Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by......
期刊： EVOLUTIONARY COMPUTATION, 2021; 29 (1)
An objective normalization strategy is essential in any evolutionary multiobjective or many-objective optimization (EMO or EMaO) algorithm, due to the......
期刊： EVOLUTIONARY COMPUTATION, 2021; 29 (1)
Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelli......
期刊： EVOLUTIONARY COMPUTATION, 2021; 29 (1)
Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programmin......
期刊： EVOLUTIONARY COMPUTATION, 2020; 28 (1)
Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of pop......
期刊： EVOLUTIONARY COMPUTATION, 2020; 28 (1)
Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inabilit......
期刊： EVOLUTIONARY COMPUTATION, 2020; 28 (2)
The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc r......
期刊： EVOLUTIONARY COMPUTATION, 2020; 28 (2)
The quality of solution sets generated by decomposition-based evolutionary multi-objective optimisation (EMO) algorithms depends heavily on the consis......
期刊： EVOLUTIONARY COMPUTATION, 2020; 28 (3)
Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained m......
期刊： EVOLUTIONARY COMPUTATION, 2019; 27 (2)
For a many-objective optimization problem with redundant objectives, we propose two novel objective reduction algorithms for linearly and, nonlinearly......
期刊： EVOLUTIONARY COMPUTATION, 2019; 27 (4)
Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in Kratsch and Neumann (2013) in the context of para......
期刊： EVOLUTIONARY COMPUTATION, 2018; 26 (1)
Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For opti......
期刊： EVOLUTIONARY COMPUTATION, 2018; 26 (2)
In real-world optimization tasks, the objective (i. e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties.......
期刊： EVOLUTIONARY COMPUTATION, 2018; 26 (4)
For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered an effective strategy to decompose the problem into sma......
