Search results for "Metaheuristic"
showing 3 items of 153 documents
Recommender system for combination of learning elements in mobile environment
2012
5 pages; International audience; The paper presents an ongoing research about the development of a new recommender system dedicated to m-learning. This system is an extension of content based recommender system proposals. It's made of three levels architecture: 1/ a domain model describing the knowledge of teaching, 2/ a user model defining learner's profile and learning's context, 3/ an adaptation model containing rules and metaheuristics, which aims at combining learning modules. Our system takes into account the spatio-temporal context of the learners, the evolution of learner's profile and the dynamic adaptation of modules during the learning process in a mobile environment. The result …
Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives
2013
A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat - a perennial problem in genetic …
Feature Extractors for Describing Vehicle Routing Problem Instances
2016
The vehicle routing problem comes in varied forms. In addition to usual variants with diverse constraints and specialized objectives, the problem instances themselves – even from a single shared source - can be distinctly different. Heuristic, metaheuristic, and hybrid algorithms that are typically used to solve these problems are sensitive to this variation and can exhibit erratic performance when applied on new, previously unseen instances. To mitigate this, and to improve their applicability, algorithm developers often choose to expose parameters that allow customization of the algorithm behavior. Unfortunately, finding a good set of values for these parameters can be a tedious task that…