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 …
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 …
Pareto optimization for subset selection with dynamic cost constraints
We consider the subset selection problem for function f with constraint bound B that changes
over time. Within the area of submodular optimization, various greedy approaches are …
over time. Within the area of submodular optimization, various greedy approaches are …
Evolutionary algorithms for the chance-constrained knapsack problem
Evolutionary algorithms have been widely used for a range of stochastic optimization
problems. In most studies, the goal is to optimize the expected quality of the solution …
problems. In most studies, the goal is to optimize the expected quality of the solution …
[HTML][HTML] Flexible wolf pack algorithm for dynamic multidimensional knapsack problems
H Wu, R **ao - Research, 2020 - spj.science.org
Optimization problems especially in a dynamic environment is a hot research area that has
attracted notable attention in the past decades. It is clear from the dynamic optimization …
attracted notable attention in the past decades. It is clear from the dynamic optimization …
Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives
Real-world combinatorial optimization problems are often stochastic and dynamic.
Therefore, it is essential to make optimal and reliable decisions with a holistic approach. In …
Therefore, it is essential to make optimal and reliable decisions with a holistic approach. In …
Sliding window bi-objective evolutionary algorithms for optimizing chance-constrained monotone submodular functions
Variants of the GSEMO algorithm using multi-objective formulations have been successfully
analyzed and applied to optimize chance-constrained submodular functions. However, due …
analyzed and applied to optimize chance-constrained submodular functions. However, due …
Multi-objective evolutionary approaches for the knapsack problem with stochastic profits
Uncertainties in real-world problems impose a challenge in finding reliable solutions. If
mishandled, they can lead to suboptimal or infeasible solutions. Chance constraints are a …
mishandled, they can lead to suboptimal or infeasible solutions. Chance constraints are a …
Runtime analysis of the (1+ 1) evolutionary algorithm for the chance-constrained knapsack problem
The area of runtime analysis has made important contributions to the theoretical
understanding of evolutionary algoirthms for stochastic problems in recent years. Important …
understanding of evolutionary algoirthms for stochastic problems in recent years. Important …
On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Evolutionary algorithms have been shown to obtain good solutions for complex optimization
problems in static and dynamic environments. It is important to understand the behaviour of …
problems in static and dynamic environments. It is important to understand the behaviour of …