Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
The shifting bottleneck procedure for job shop scheduling
Management Science
The Vision of Autonomic Computing
Computer
Cooperative Case-Based Reasoning
ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Towards a paradigm change in computer science and software engineering: a synthesis
The Knowledge Engineering Review
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/Crc Computer & Information Science Series)
Autonomic Computing
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
Case-based selection of initialisation heuristics for metaheuristic examination timetabling
Expert Systems with Applications: An International Journal
Ant colony intelligence in multi-agent dynamic manufacturing scheduling
Engineering Applications of Artificial Intelligence
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers
Computers and Operations Research
A hybrid metaheuristic case-based reasoning system for nurse rostering
Journal of Scheduling
Principles of Sequencing and Scheduling
Principles of Sequencing and Scheduling
Approximation algorithms for multi-agent scheduling to minimize total weighted completion time
Information Processing Letters
A survey of dynamic scheduling in manufacturing systems
Journal of Scheduling
Towards autonomic computing: adaptive job routing and scheduling
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A hybrid immune simulated annealing algorithm for the job shop scheduling problem
Applied Soft Computing
A multi-objective ant colony system algorithm for flow shop scheduling problem
Expert Systems with Applications: An International Journal
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
Applied Soft Computing
Competitive Two-Agent Scheduling and Its Applications
Operations Research
ICCNT '10 Proceedings of the 2010 Second International Conference on Computer and Network Technology
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Autonomic job scheduling policy for grid computing
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Job scheduling for maximal throughput in autonomic computing systems
IWSOS'06/EuroNGI'06 Proceedings of the First international conference, and Proceedings of the Third international conference on New Trends in Network Architectures and Services conference on Self-Organising Systems
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence
Runtime estimation using the case-based reasoning approach for scheduling in a grid environment
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
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Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Meta-heuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Meta-heuristics and for the resolution of scheduling problems on dynamic environments.