A survey on recent progress in the theory of evolutionary algorithms for discrete optimization
The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …
progress since the early 2010s. This survey summarizes some of the most important recent …
Effective 2-and 3-objective MOEA/D approaches for the chance constrained knapsack problem
Optimizing real-world problems often involves decision-making under uncertainty due to the
presence of unknown or uncontrollable variables. Chance-constraints allow to model the …
presence of unknown or uncontrollable variables. Chance-constraints allow to model the …
Multi-objective evolutionary algorithms with sliding window selection for the dynamic chance-constrained knapsack problem
Evolutionary algorithms are particularly effective for optimisation problems with dynamic and
stochastic components. We propose multi-objective evolutionary approaches for the …
stochastic components. We propose multi-objective evolutionary approaches for the …
The chance constrained travelling thief problem: Problem formulations and algorithms
The travelling thief problem (TTP) is a multi-component combinatorial optimization problem
that has gained significant attention in the evolutionary computation and heuristic search …
that has gained significant attention in the evolutionary computation and heuristic search …
Sampling-based Pareto optimization for chance-constrained monotone submodular problems
Recently surrogate functions based on the tail inequalities were developed to evaluate the
chance constraints in the context of evolutionary computation and several Pareto …
chance constraints in the context of evolutionary computation and several Pareto …
Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions
Submodular functions allow to model many real-world optimisation problems. This paper
introduces approaches for computing diverse sets of high quality solutions for submodular …
introduces approaches for computing diverse sets of high quality solutions for submodular …
Sparsity preserved Pareto optimization for subset selection
L Zhang, X Sun, H Yang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Subset selection that selects a limited number of variables optimizing many given criteria is
a fundamental problem with various applications, such as sparse regression (SR) and …
a fundamental problem with various applications, such as sparse regression (SR) and …
Runtime analysis of RLS and the (1+ 1) EA for the chance-constrained knapsack problem with correlated uniform weights
Addressing a complex real-world optimization problem is a challenging task. The chance-
constrained knapsack problem with correlated uniform weights plays an important role in the …
constrained knapsack problem with correlated uniform weights plays an important role in the …
Evolutionary algorithms for limiting the effect of uncertainty for the knapsack problem with stochastic profits
Evolutionary algorithms have been widely used for a range of stochastic optimization
problems in order to address complex real-world optimization problems. We consider the …
problems in order to address complex real-world optimization problems. We consider the …
3-objective pareto optimization for problems with chance constraints
Evolutionary multi-objective algorithms have successfully been used in the context of Pareto
optimization where a given constraint is relaxed into an additional objective. In this paper …
optimization where a given constraint is relaxed into an additional objective. In this paper …