Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …
techniques into meta-heuristics for solving combinatorial optimization problems. This …
Machine learning into metaheuristics: A survey and taxonomy
EG Talbi - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …
effective, and robust metaheuristics has become increasingly popular. Many of those …
Evolutionary transfer optimization-a new frontier in evolutionary computation research
The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …
works on Darwinian principles of natural selection. Due to its strong search capability and …
Insights on transfer optimization: Because experience is the best teacher
Traditional optimization solvers tend to start the search from scratch by assuming zero prior
knowledge about the task at hand. Generally speaking, the capabilities of solvers do not …
knowledge about the task at hand. Generally speaking, the capabilities of solvers do not …
Genetic algorithms
K Sastry, D Goldberg, G Kendall - Search methodologies: Introductory …, 2005 - Springer
Chapter 4 GENETIC ALGORITHMS Page 1 Chapter 4 GENETIC ALGORITHMS Kumara Sastry,
David Goldberg University of Illinois, USA Graham Kendall University of Nottingham, UK 4.1 …
David Goldberg University of Illinois, USA Graham Kendall University of Nottingham, UK 4.1 …
Evolutionary computation meets machine learning: A survey
Evolutionary computation (EC) is a kind of optimization methodology inspired by the
mechanisms of biological evolution and behaviors of living organisms. In the literature, the …
mechanisms of biological evolution and behaviors of living organisms. In the literature, the …
A knowledge-based ant colony optimization for flexible job shop scheduling problems
A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper
for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an …
for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an …
Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
This paper reviews the existing literature on the combination of metaheuristics with machine
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …
Evolutionary multitask optimization: a methodological overview, challenges, and future research directions
In this work, we consider multitasking in the context of solving multiple optimization problems
simultaneously by conducting a single search process. The principal goal when dealing with …
simultaneously by conducting a single search process. The principal goal when dealing with …
Multiproblem surrogates: Transfer evolutionary multiobjective optimization of computationally expensive problems
In most real-world settings, designs are often gradually adapted and improved over time.
Consequently, there exists knowledge from distinct (but possibly related) design exercises …
Consequently, there exists knowledge from distinct (but possibly related) design exercises …