Titlebook: Learning and Intelligent Optimization; 9th International Co Clarisse Dhaenens,Laetitia Jourdan,Marie-Eléonore Conference proceedings 2015

Enclosure

書目名稱Learning and Intelligent Optimization影響因子(影響力)




書目名稱Learning and Intelligent Optimization影響因子(影響力)學(xué)科排名




書目名稱Learning and Intelligent Optimization網(wǎng)絡(luò)公開度




書目名稱Learning and Intelligent Optimization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Learning and Intelligent Optimization被引頻次




書目名稱Learning and Intelligent Optimization被引頻次學(xué)科排名




書目名稱Learning and Intelligent Optimization年度引用




書目名稱Learning and Intelligent Optimization年度引用學(xué)科排名




書目名稱Learning and Intelligent Optimization讀者反饋




書目名稱Learning and Intelligent Optimization讀者反饋學(xué)科排名

駕駛

IST

Overstate

裝入膠囊

,Dynamic Service Selection with Optimal Stopping and ‘Trivial Choice’,Two different strategies for searching a best-available service in adaptive, open software systems are simulated. The practical advantage of the theoretically optimal strategy is confirmed over a ‘trivial choice’ approach, however the advantage was only small in the simulation.

Antecedent

ARBOR

https://doi.org/10.1007/978-3-319-19084-6Algorithm construction; Answer set programming; Bio-inspired approaches; Bio-inspired optimization; Clas

Cardiac

Learning a Hidden Markov Model-Based Hyper-heuristic,ng useful mutation heuristics. Empirical evidence supports this on the ., ., . and . problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach.

ALLAY

Exploring Non-neutral Landscapes with Neutrality-Based Local Search,tion. Some experiments on NK landscapes show that an adaptive discretization is useful to reach high local optima and to launch diversifications automatically. We believe that a hill-climbing using such an adaptive evaluation function could be more appropriated than a classical iterated local search mechanism.

使熄滅

A Biased Random-Key Genetic Algorithm for the Multiple Knapsack Assignment Problem,em. The MKAP is a hard problem even for small-sized instances. In this paper, we propose an approximate approach for the MKAP based on a biased random key genetic algorithm. Our solution approach exhibits competitive performance when compared to the best approximate approach reported in the literature.