An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for solving a given computationally hard problem. In this study, we investigate into a novel apprenticeship learning-based approach which is used to automatically generate a hyper-heuristic for vehicle routing. This approach itself can be considered as a hyper-heuristic which operates in a train and test fashion. A state-of-the-art hyper-heuristic is chosen as an expert which is the winner of a previous hyper-heuristic competition. Trained on small vehicle routing instances, the learning approach yields various classifiers, each capturing different actions that the expert hyper-heuristic performs during the search process. Those classifiers are then used to produce a hyper-heuristic which is potentially capable of generalizing the actions of the expert hyperheuristic while solving the unseen instances. The experimental results on vehicle routing using the Hyper-heuristic Flexible (HyFlex) framework shows that the apprenticeship-learning based hyper-heuristic delivers an outstanding performance when compared to the expert and some other previously proposed hyper-heuristics

Machine learning for improving heuristic optimisation

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance

A Novel Particle Swarm Optimization Algorithm

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2012Parçacık Sürü Optimizasyonu (Particle Swarm Optimization) (PSO) 1995’te Dr. Eberhart ve Dr. Kennedy tarafından geliştirilmiş popülasyon temelli sezgisel bir optimizasyon tekniğidir. Bu tekniğin bilinen dezavantajı, gerçek optimumu bulmadan önce erken yakınsama sergilenmesidir. Bu çalışmada, literatürde geleneksel hale gelen ve yeni parçacıkları popülasyona yeniden enjekte etmeyi öneren yöntemlere karşın, parçacıkları harici belleklerden alınan bilgilere göre yönlendiren yeni bir parçacık sürü optimizasyon algoritmasını sunmaktadır. Bunun için, parçacığın harici bellekteki parçacıklar arasından, parçacığa en yakın olan en iyi ve en kötü parçacığa uzaklığı hesaplanıp bir katsayi üretilmektedir. Sonra her parçacık için hız bileşenini hesaplarken bu katsayı belli bir olasılıkla mevcut hıza eklenmektedir. Ayrıca randomize bir üst ve alt sınır inertia için tanınmaktadır. Algoritmada inertia bileşeni üst sınırden başlayıp ve her parçacığı değerlendirdikten sonra küçük bir değerle non-linear bir şekilde azaltılmaktadır. Inertia bileşeni randomize alt sınıra ulaştığı zaman, bu bileşenin değeri randomize üst sınırın değeriyle sıfırlamaktadır. Çıkan PSO evrensel optima’yi orijinal PSO’den daha hızlı bulmaktadır. Bu algoritma en güncel PSO’ların arasında olan CLPSO algoritmasından daha hızlı olduğunu ve daha kaliteli çözümler ürettiğini ortaya çıkarılmıştır. Ayrıca literatürde en iyi ve güncel optimizasyon algoritmaların arasında olan CMA-ES algoritması de kıyaslamak amacıyla seçilmiştir. Deneylerin sonucunda, CMA-ES’in genelde PSO’den daha iyi olmasına rağmen, bazi durumlarda, sunulan yöntemin daha üstün bir performans sergilediği ortaya çıkarılmıştır. Evrensel optima’yı gösteren evrensel bir topolojinin eksik olduğu veya havzasının çeker hacmı küçük olan problemlerde, sunulan algoritma daha hızlı davrandığı gösterilmiştir. Deneyler, standart kriter olan fonksyonlar ve simülatör ortamındaki Aldebaran NAO robotun kick hareketi için uygulamaktadır.Particle Swarm Optimization (PSO) (Kennedy and Eberhart, 1995), which is a population-based global search method is known to suffer from premature convergence prior to discovering the true global minimizer. In this thesis, a novel memory-based method is proposed which aims to guide the particles through the information deduced from the external memory contents rather than to re-inject them into the population. This is done by calculating a coefficient, based on the distance of the current particle to the closest best and closest worst particles in the external memory at each iteration. Later, when updating the velocity component, this coefficient is added to the current velocity of the particle with a certain probability. Also randomized upper bound and lower bound values have been defined for the inertia component. The algorithm starts with the upper bound value of the inertia. At each particle evaluation the inertia is decreased non-linearly with a small value and when its value reaches the lower bound, the inertia value is reset to its upper bound. The resulting PSO finds the global optima much faster than the original PSO and it have been shown that it also performs better compared with a recent improvement of PSO, CLSPO namely. A state-of-the-art algorithm, CMA-ES (Covariance Matrix Adaptation Evolutionary Strategy), has also been chosen for comparison purposes. It has been shown by experiments that although the CMA-ES shows a better performance than that of our algorithm, in some cases where the overall topology pointing to the global optimum is missing and the attractor volume of global optimum is small, our algorithm performs better and finds the desired optimum value of the function in lesser evaluation counts. The tests have been consucted on standard benchmark functions as well as a simulation of the Aldebaran NAO robot for developing a kick action.Yüksek LisansM.Sc

Soft morphological filter optimization using a genetic algorithm for noise elimination

Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well

Soft morphological filter optimization using a genetic algorithm for noise elimination

Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well

An investigation on test driven discrete event simulation

This paper deals with the application of modern software development tools on simulation development. Recently, Agile Software Development (ASD) methods enjoy an increasing popularity. eXtreme Programming (XP) techniques, one of the techniques which belong to the ASD group of methods is a software development method which improves software quality and responsiveness of software projects through introducing short development cycles and a Test Driven Development (TDD) philosophy throughout the development. In this paper, we particularly pay attention to the application of the TDD by approaching simulation development from a test-first perspective. This study consists of a feasibility study of applying the TDD technique in simulation development in its various levels, say, acceptance and unit testing. Moreover, a simulation case study of a surgical ward has been considered, designed and implemented using the AnyLogic simulation toolkit. Our study differs from the mainstream in many ways. It addresses the feasibility of Test-Driven Simulation Development in Visual Interactive Modelling and Simulation (VIMS) environments as well as providing an insight into how the test-first concept can further help with the choice of components and acceptance testing

Machine learning for improving heuristic optimisation

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance

An investigation on test driven discrete event simulation

This paper deals with the application of modern software development tools on simulation development. Recently, Agile Software Development (ASD) methods enjoy an increasing popularity. eXtreme Programming (XP) techniques, one of the techniques which belong to the ASD group of methods is a software development method which improves software quality and responsiveness of software projects through introducing short development cycles and a Test Driven Development (TDD) philosophy throughout the development. In this paper, we particularly pay attention to the application of the TDD by approaching simulation development from a test-first perspective. This study consists of a feasibility study of applying the TDD technique in simulation development in its various levels, say, acceptance and unit testing. Moreover, a simulation case study of a surgical ward has been considered, designed and implemented using the AnyLogic simulation toolkit. Our study differs from the mainstream in many ways. It addresses the feasibility of Test-Driven Simulation Development in Visual Interactive Modelling and Simulation (VIMS) environments as well as providing an insight into how the test-first concept can further help with the choice of components and acceptance testing

Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing

A hyper-heuristic is a heuristic optimisation method which generates or selects heuristics (move operators) based on a set of components while solving a computationally difficult problem. Apprenticeship learning arises while observing the behavior of an expert in action. In this study, we use a multilayer perceptron (MLP) as an apprenticeship learning algorithm to improve upon the performance of a state-of-the-art selection hyper-heuristic used as an expert, which was the winner of a cross-domain heuristic search challenge (CHeSC 2011). We collect data based on the relevant actions of the expert while solving selected vehicle routing problem instances from CHeSC 2011. Then an MLP is trained using this data to build a selection hyper-heuristic consisting of a number classifiers for heuristic selection, parameter control, and move-acceptance. The generated selection hyper-heuristic is tested on the unseen vehicle routing problem instances. The empirical results indicate the success of MLP-based hyper-heuristic achieving a better performance than the expert and some previously proposed algorithms

Heuristic generation via parameter tuning for online bin packing

Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a ‘policy matrix’ representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem