Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Many decision and optimization problems arising in bioinformatics field are time demanding, and several algorithms are designed to solve these problems or to improve their current best solution approach. Modeling and implementing a new heuristic algorithm may be time‐consuming but has strong motivations: on the one hand, even a small improvement of the new solution may be worth the long time spent on the construction of a new method; on the other hand, there are problems for which good‐enough solutions are acceptable which could be achieved at a much lower computational cost. In the first case, specially designed heuristics or metaheuristics are needed, while the latter hyper‐heuristics can be proposed. The paper will describe both approaches in different domain problems

A Lifelong Learning Hyper-heuristic Method for Bin Packing.

We describe a novel Hyper-heuristic system which continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; representative problems and heuristics are incorporated into a self-sustaining network of interacting entities in- spired by methods in Artificial Immune Systems.The network is plastic in both its structure and content leading to the following properties: it exploits existing knowl- edge captured in the network to rapidly produce solutions; it can adapt to new prob- lems with widely differing characteristics; it is capable of generalising over the prob- lem space. The system is tested on a large corpus of 3968 new instances of 1D-bin packing problems as well as on 1370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to pre- vious approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable

Optimización del trazado de patrones de corte

Este Trabajo de Fin de Grado pretende considerar soluciones algorítmicas al problema de optimizar la producción de piezas bidimensionales de un determinado material recortadas dentro de una cadena de producción, de tal manera que se minimice la cantidad de material desperdiciado en el proceso de corte. El objetivo básico es minimizar el área utilizada para recortar estas piezas bajo, entre otros, los siguientes supuestos • Se considerará un material madre donde efectuar los recortes de forma rectangular con una anchura prefijada y longitud ilimitada. • Se considerará material desperdiciado todo aquel que se encuentre dentro de la longitud mínima del material madre que contenga todas las piezas y no se encuentre encerrado en el área de alguna pieza. • Se considerará una colocación correcta de las piezas de manera que todas estén dentro del material madre, sin colisiones entre ellas y sin que una esté contenida en el interior de ninguna de las demás. • Se consideran piezas delimitadas por rectas y curvas relativamente simples. Un algoritmo debe aportar un número determinado de piezas dispuestas para su colocación en un determinado orden en el material madre, y su eficacia se medirá tanto por el porcentaje de área no desperdiciada como por el tiempo necesario para obtener su colocación. Para ello se divide el problema en tres fases: selección de pieza a introducir, proceso de colocación de dicha pieza y control de sus posibles colisiones. Todo ello ha de tener en cuenta las piezas ya colocadas y su posición, repitiéndose el proceso de colocación probando tal vez diferentes criterios de ordenación que mejoren el resultado hasta un límite de iteraciones previamente fijado. Entre las técnicas que se vienen empleando están el método de colocación Bottom-left mediante vectores corredizos, las estrategias de prevención de colisión mediante cuadro delimitador y aproximación rectangular, así como el algoritmo NEAT, basado en técnicas evolutivas genéticas. Las mismas se considerarán en el trabajo, con un énfasis en el algoritmo NEAT

Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms

Many approaches to applying Genetic Algorithms (GAs) to Nondeterministic Polynomial time Complete (NPC) problems involve population members encoded directly from the problem solution space. While this technique enables trivial mapping of the population members to solutions, it can cause complex problems for GA operators as they attempt to direct the evolution of the population toward more promising areas of the solution space. These operators, using inspiration from genetics and evolution in the biological world, combine and manipulate the current population to produce a new population that, it is hoped, will eventually converge toward better solutions to the original problem. However, many problems, especially graph-space problems, cannot be so easily manipulated when GA members consist of direct encodings. In such cases, GA operators must perform awkward transformations to convert the progeny into viable solutions. Here is where heuristic encoding comes into play, in that any combination of genes will produce a viable solution. However, this additional level of abstraction does cause other problems and tends to weaken the guiding effects of traditional GA operators. Thus, I have designed custom GA operators that mitigate these problems by using the solutions produced by the heuristic encoded members to better guide the manipulation when producing the next generation. This dissertation shows that heuristic encoding is an effective technique for the representation of solutions to graph-space problems. It also shows that, when using heuristic encoding, GAs with traditional operators perform well compared to more direct encoding techniques. Finally it shows the combination of heuristic encoding and GA operators designed to work with them increases GA performance and can be competitive with other techniques. I believe that these techniques will also work well for other types of problems for which GAs are commonly applied