Hugo Terashima-Marín

Learn More
The idea behind hyper-heuristics is to discover rules that relate different problem states with the best single heuristic to apply. This investigation works towards extending the problem domain in which a given hyper-heuristic can be applied and implements a framework to generate hyper-heuristics for a wider range of bin packing problems. We present a(More)
This paper presents a method for combining concepts of Hyper-heuristics and Learning Classifier Systems for solving 2D Cutting Stock Problems. The idea behind Hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. In this(More)
The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GA-based method that produces general hyper-heuristics that solve two-dimensional cutting stock problems. The GA uses a(More)
— Hyper-heuristics are high level search method-ologies that operate over a set of heuristics which operate directly on the problem domain. In one of the hyper-heuristic frameworks, the goal is automating the process of selecting a human-designed low level heuristic at each step to construct a solution for a given problem. Constraint Satisfaction Problems(More)
The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GA-based method that produces general hyper-heuristics for the dynamic variable ordering within Constraint Satisfaction Problems.(More)
The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents two Evolutionary-Computation-based Models to producehyper-heuristics that solve two-dimensional bin-packing problems. The first model(More)