Evolutionary algorithms + domain knowledge = real-world evolutionary computation

Bonissone, Piero P.; Subbu, Raj; Eklund, Neil; Kiehl, Thomas R.

doi:10.1109/tevc.2005.857695

Cited by 128 publications

(65 citation statements)

References 69 publications

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“…In addition, it is also being increasingly realized that EAs without incorporation of problem-specific knowledge do not perform as well as mathematical programming based algorithms on certain classes of timetabling problems [13]. In this paper, we aim to combine the good properties of local and global area based algorithms to solve the PECTP.…”

Section: Related Workmentioning

confidence: 99%

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Jat

Yang

2010

J Sched

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The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimization problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.

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Section: Related Workmentioning

confidence: 99%

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Jat

Yang

2010

J Sched

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“…The various heuristics used for initializing the population, designing the genetic operators and controlling the GA parameters are based on a structured approach to representing domain knowledge, as described in [25]. This approach allows the use, both implicitly and explicitly, of the knowledge we have on the optimization problem.…”

Section: Implementation Strategymentioning

confidence: 99%

Genetic algorithm–based training for semi-supervised SVM

Adankon

Cheriet

2010

Neural Comput & Applic

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The Support Vector Machine (SVM) is an interesting classifier with excellent power of generalization. In this paper, we consider applying the SVM to semi-supervised learning. We propose using an additional criterion with the standard formulation of the semi-supervised SVM (S 3 VM) to reinforce classifier regularization. Since, we deal with nonconvex and combinatorial problem, we use a genetic algorithm to optimize the objective function. Furthermore, we design the specific genetic operators and certain heuristics in order to improve the optimization task. We tested our algorithm on both artificial and real data and found that it gives promising results in comparison with classical optimization techniques proposed in literature.

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“…We trained [14,39] each AANN separately, within its region of competence, and we defined a set of fuzzy transition rules for the supervisory controller. Then we defined an Offline MH, using an Evolutionary Algorithm to tune the parameters of the membership functions of the fuzzy supervisory to minimize a figure of merit that aggregated all the residuals during normal conditions.…”

Section: Offline Mh's (Evolutionary Algorithms)mentioning

confidence: 99%

Soft Computing: A Continuously Evolving Concept

Bonissone¹

2010

IJCIS

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Abstract. Soft Computing (SC) is a concept with constantly evolving semantics, as researchers have adopted its main philosophy while adding various interpretations and facets to this concept. Originally defined as a loose association or partnership of components, SC has gone through several transformational phases. This paper will trace some of the phases experienced by the author as part of his understanding of the evolution of SC and its role in constructing decision-making models. The first phase is the hybridization phase, driven by the inherit ease of integration of SC components. The second phase is a two-level model characterization, based on the split between object-level and meta-level reasoning. This phase, inspired by traditional AI problem formulation, led to a third phase, in which we addressed the knowledge and meta-knowledge representation required by each of these reasoning levels using a linguistics analogy. The fourth phase is the extension of the heuristics used at the metalevel, e.g. Metaheuristics (MH's) from evolutionary algorithms to other search methods. The fifth and last phase, further described in this paper, is the proposal for a strong separation between offline MH's (used for design and tuning) and online MH's (used for models selection or aggregation.) This last view suggests a broader use of SC components, since it enables us to use hybrid SC techniques at each of the MH's levels as well as at the object level. Furthermore, this separation facilitates the model lifecycle management, which is required to maintain the models vitality and prevent their obsolescence over time.

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Evolutionary algorithms + domain knowledge = real-world evolutionary computation

Cited by 128 publications

References 69 publications

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Genetic algorithm–based training for semi-supervised SVM

Soft Computing: A Continuously Evolving Concept

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