There is an emerging trend towards the automated design of metaheuristics at the software component level. In principle, metaheuristics have a relatively clean decomposition, where well-known frameworks such as ILS and EA are parametrised by variant components for acceptance, perturbation etc. Automated generation of these frameworks is not so simple in practice, since the coupling between components may be implementation specific. Compositionality is the ability to freely express a space of designs ‘bottom up’ in terms of elementary components: previous work in this area has used combinators, a modular and functional approach to componentisation arising from foundational Computer Science. In this article, we describeHaiku, a combinator too...
Following decades of sustained improvement, metaheuristics are one of the great success stories of o...
This paper is concerned with taking an engineering approach towards the application of metaheuristic...
Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the...
There is an emerging trend towards the automated design of metaheuristics at the software component ...
There is a pressing need for a higher-level architectural per- spective in metaheuristics research. ...
Metaheuristics have gained great success in academia and practice because their search logic can be ...
This talk provides a complete background on metaheuristics and presents in a unified view the main d...
Design patterns capture the essentials of recurring best practice in an abstract form. Their merits ...
A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic methods...
Nowadays, many engineering applications require the minimization of a cost function such as decreasi...
Following decades of sustained improvement, metaheuristics are one of the great success stories of o...
Abstract: Hybrids of meta-heuristics have been shown to be more effective and adaptable than their p...
In this Chapter, we consider the hybridization of column generation (CG) with metaheuristics (MHs) f...
While meta-heuristics are effective for solving large-scale combinatorial optimization problems, the...
This paper presents MAHF, a software framework for the highly flexible construction of metaheuristic...
Following decades of sustained improvement, metaheuristics are one of the great success stories of o...
This paper is concerned with taking an engineering approach towards the application of metaheuristic...
Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the...
There is an emerging trend towards the automated design of metaheuristics at the software component ...
There is a pressing need for a higher-level architectural per- spective in metaheuristics research. ...
Metaheuristics have gained great success in academia and practice because their search logic can be ...
This talk provides a complete background on metaheuristics and presents in a unified view the main d...
Design patterns capture the essentials of recurring best practice in an abstract form. Their merits ...
A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic methods...
Nowadays, many engineering applications require the minimization of a cost function such as decreasi...
Following decades of sustained improvement, metaheuristics are one of the great success stories of o...
Abstract: Hybrids of meta-heuristics have been shown to be more effective and adaptable than their p...
In this Chapter, we consider the hybridization of column generation (CG) with metaheuristics (MHs) f...
While meta-heuristics are effective for solving large-scale combinatorial optimization problems, the...
This paper presents MAHF, a software framework for the highly flexible construction of metaheuristic...
Following decades of sustained improvement, metaheuristics are one of the great success stories of o...
This paper is concerned with taking an engineering approach towards the application of metaheuristic...
Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the...