Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

/pdf/hyper-heuristics-a-survey-of-the-state-of-the-art-mwp7dhxfb3.pdf

Hyper-heuristics: a survey of the state of the art

A genetic algorithm for the Flexible Job-shop Scheduling Problem

https://sci2s.ugr.es/keel/pdf/specific/articulo/scopus64.pdf

Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories-however, if the field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the past, and ask the difficult question ''was it worth it ?''. We then attempt to suggest a set of problem features that we believe will allow the true potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS.

Application areas of AIS: The past, the present and the future

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the
paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the
field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to
be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the
past, and ask the difficult question ‘‘was it worth it ?’’. We then attempt to suggest a set of problem features that we believe will allow the true
potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS

Application areas of AIS: the past, present and future.

This work presents an efficient methodology called GENACE for solving the flexible job-shop scheduling problem (or FJSP) with recirculation. We show how CDRs are used to solve the FJSP with recirculation by themselves and to provide a bootstrapping mechanism to initialize GENACE. We then adopt a cultural evolutionary architecture to maintain knowledge of schemata and resource allocations learned over each generation. The belief spaces influence mutation and selection over a feasible chromosome representation. Experimental results show that GENACE obtains better upper bounds for 11 out of 13 benchmark problems, with improvement factors of 2 to 48 percent when compared to results by Kacem et al. (2002), Brandimarte (1993) and of using CDRs alone.

GENACE: an efficient cultural algorithm for solving the flexible job-shop problem

We solve the flexible job shop problem (FJSP) by using dispatching rules discovered through genetic programming (GP). While simple priority rules (SPR) have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules (CDR) have been shown to be more effective as they are constructed through human experience. In this paper, we employ suitable parameter and operator spaces for evolving CDRs using GP, with an aim towards greater scalability and flexibility. Experimental results show that CDRs generated by our GP framework outperforms the SPRs and CDRs selected from literature in 74% to 85% of FJSP problem instances.

/pdf/evolving-dispatching-rules-for-solving-the-flexible-job-shop-2tnd28ru8r.pdf

Evolving dispatching rules for solving the flexible job-shop problem

The interaction between evolution and learning has received much attention with recent studies in machine learning showing that it can significantly improve the efficiency of evolutionary strategies for job-shop scheduling We propose a tripartite architecture called LEGA; comprising a population generator that improves the quality of the initial population for subsequent evolution while training a schemata learning module to modify the fitnesses of its offsprings aided by a memory of conserved schemas resolved from sampled schedules received dynamically during evolution Experimental results indicate that an instantiation of LEGA outperforms current approaches using canonical EAs in computational time and quality of schedules

Nhu Binh Ho

Papers

GENACE: an efficient cultural algorithm for solving the flexible job-shop problem

Evolving dispatching rules for solving the flexible job-shop problem

LEGA: an architecture for learning and evolving flexible job-shop schedules