The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyper-heuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high level heuristics during the problem solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism which contains a population of both high quality and diverse solutions that is updated during the problem solving process. The generality of the proposed hyper-heuristic is validated against six well known combinatorial optimization problem, with very different landscapes, provided by the HyFlex software. Empirical results comparing the proposed hyper-heuristic with state of the art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite

Design Of Perturbative Hyper-Heuristics For Combinatorial Optimisation

Combinatorial optimisation is an area which seeks to identify optimal solution(s) from a discrete solution search space. Approaches for solving combinatorial optimisation problems can be separated into two main sub-classes, i.e. exact and approximation algorithms. Exact algorithm is a sub-class of techniques that is able to guarantee global optimality. However, exact algorithms are not feasible for solving complex problem due to its high computational overhead. Approximation algorithm is a sub-class of techniques which is able to provide sub-optimal solution(s) with reasonable computational cost. In order to explore the solution search space of a combinatorial optimisation problem, an approximation algorithm performs perturbations on the existing solutions by adopting a single or multiple perturbative Low-Level Heuristic(s) (LLHs). The use of a single LLH leads to poor performance when the particular heuristic is incompetent in solving the problem. Thus, the use of multiple LLHs is more desirable as the weaknesses of one heuristic can be compensated by the strengths of another. When there are multiple LLHs, a hyper-heuristic can be integrated to determine the choice of heuristics for a particular problem or situation. Hyper-heuristic automates the selection of LLHs through a high-level heuristic that consists of two key components, i.e. a heuristic selection method and a move acceptance method. The capability of a high-level heuristic is highly problem dependent as the landscape properties of a problem are unique among others. The high-level heuristics in the existing hyper-heuristics are designed by manually matching different combinations of high-level heuristic components

The General Combinatorial Optimization Problem: Towards Automated Algorithm Design

This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimisation problems. Despite recent advances, the evolutionary computation and optimisation research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardise various search algorithms as well as selection hyper-heuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design

Grammatical evolution hyper-heuristic for combinatorial optimization problems

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains

Learning heuristic selection using a time delay neural network for open vehicle routing

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As a chosen ‘expert’ hyper-heuristic is run on a small set of training problem instances, data is collected to learn from the expert regarding how to decide which low-level heuristic to select and apply to the solution in hand during the search process. In this study, a Time Delay Neural Network (TDNN) is used to extract hidden patterns within the collected data in the form of a classifier ,i.e an ‘apprentice’ hyper-heuristic, which is then used to solve the ‘unseen’ problem instances. Firstly, the parameters of TDNN are tuned using Taguchi orthogonal array as a design of experiments method. Then the influence of extending and enriching the information collected from the expert and fed into TDNN is explored on the behaviour of the generated apprentice hyper-heuristic. The empirical results show that the use of distance between solutions as an additional information collected from the expert generates an apprentice which outperforms the expert algorithm on a benchmark of OVRP instances

Deep Learning and Deep Reinforcement Learning for Graph Based Applications

Dyp læring har gitt state-of-the-art ytelse i mange applikasjoner som datasyn, tekstanalyse, biologi, osv. Suksessen med dyp læring har også hjulpet fremveksten av dyp forsterkende læring for optimal beslutningstaking og har vist stort potensiale, spesielt i optimaliseringsproblemer. I tillegg har grafer som matematisk representasjon for strukturerte komplekse systemer vist seg å være et kraftig verktøy for analyse og problemløsning, og gitt et nytt perspektiv på formuleringen av problemet. Ved å introdusere grafer som en inputmodalitet for maskinlæringsproblemer kan dyplæringsmodeller enten bruke strukturen til grafen i sine representasjonslæringsskjema, eller optimalisere grafstrukturen i en nedstrøms evalueringsoppgave. Dette vil også føre til modellmetoder og pipelines som utnytter den strukturelle informasjonen gitt av grafer til forbedret ytelse, sammenlignet med tradisjonelle maskinlæringsmodellers kapasitet. I denne oppgaven introduserer vi fem forskjellige use-case-applikasjoner, gjennom fem forskningsartikler, som kan modelleres som grafer og tar sikte på å skape nye modeller som adresserer problemer ved bruk av dyp grafrepresentasjonslæring og dype forsterkningslæringsmodeller. Våre tre viktigste applikasjonsdomener er bioinformatikk, datasyn og logistikk. Først tar vi sikte på å adressere to problemer innen bioinformatikk. I Paper I tar vi opp spørsmålet om integrering av kontinuerlige omics-datasett med biologiske nettverk. Vi introduserer et auto-koderskjema fokusert på representasjonslæring av nodefunksjoner i biologiske nettverk, og viser anvendelsen av det utformede rammeverket i et virkelighetseksempel gjennom imputering av manglende verdier i et eksempeldatasett for omics. Paper II ser på bruk av grafrepresentasjonslæring for å behandle metabolske nettverk. I den foreslåtte tilnærmingen introduserer vi en maskinlæringspipeline (fra funksjonsekstraksjon til modellarkitektur) basert på grafiske nevrale nettverk og evaluerer pipelinen basert på prediksjon av genessensalitet, som er en velkjent bruk av metabolske banenettverk. Det andre domenet av applikasjoner er datasynsdomenet, spesifikt problemet med gjenkjennelse av menneskelige gester. I Paper III, og oppfølgingen Paper IV, introduserer vi et gestgjenkjenningssystem som er både raskere og mer nøyaktig enn den avanserte prediksjonen av menneskelige motivbevegelser fra mmWave Radar genererte punktskyer. Vi oppnår dette ved å modellere inngangspunktskyen som en spatio-temporal graf og å bearbeide den opprettede grafen ved bruk av den foreslåtte læringsteknikken for grafrepresentasjon. Videre evaluerer vi systemet under forskjellige eksperimentelle forhold ut ifra vinkelen til emnet med hensyn til sansing, og foreslår en ensembletilnærming for å dempe effekten av å endre sansevinkelen på ytelsen til modellen. Den siste applikasjonen vi tar for oss er bruken av dyp forsterkningslæring for å optimalisere strukturen til grafene i kombinatoriske optimaliseringsproblemer i logistikk. Paper V introduserer en generell problemuavhengig hyperheuristikk som utnytter beslutningsevnen til dyp forsterkende læring, ved å bruke en problemuavhengig tilstandsfunksjonsinformasjon. Det foreslåtte rammeverket er trent på en generell belønningsfunksjon for å oppnå høykvalitets ytelse blant populære løsere innen kombinatorisk optimalisering. Vi evaluerer ytelsen til den foreslåtte tilnærmingen med tre eksempler på ruting problemer samt et planleggingsproblem, for å vise effektiviteten til metoden vår i forskjellige typer problemstillinger.Deep learning has provided state-of-the-art performance in many applications such as computer vision, text analysis, biology, etc. The success of deep learning has also helped with the emergence of deep reinforcement learning for optimal decision-making and has shown great promise, especially in optimization problems. Additionally, graphs as a mathematical representation for structured complex systems have proven to be a powerful tool for analysis and problem-solving that offer a fresh perspective on the formulation of the problem. Introducing graphs as an input modality for machine learning problems enables deep learning models to either utilize the structure of the graph in their representation learning scheme or optimize the graph structure for a downstream evaluation task. Doing so will also lead to model methods and pipelines that leverage the structural information provided by graphs to improve performance compared to traditional machine learning models. In this thesis, we introduce five different use-case applications, in the format of five research papers, that can be modeled as graphs and aim to provide novel models that address problems using deep graph representation learning and deep reinforcement learning models. Our main three application domains are bioinformatics, computer vision, and logistics. First, we aim to address two problems in the domain of bioinformatics. In Paper I, we address the issue of integration of continuous omics datasets with biological networks. We introduce an auto-encoder scheme focused on representation learning of node features in biological networks and showcase the application of the designed framework in a real-world example through the imputation of missing values in an example omics dataset. Paper II looks at utilizing graph representation learning for processing metabolic networks. In the proposed approach, we introduce a machine learning pipeline (from feature extraction to model architecture) based on graph neural networks and evaluate the pipeline on the task of gene essentiality prediction which is a well-known application of metabolic pathway networks. The second domain of applications is the computer vision domain specifically the problem of human gesture recognition. In Paper III and the follow-up Paper IV, we introduce a gesture recognition system that is both faster and more accurate compared to the state-of-the-art prediction of human subject gestures from mmWave Radar generated point clouds. We achieve this by modeling the input point cloud as a spatio-temporal graph and processing the created graph using the proposed graph representation learning technique. We further evaluate the system in different experimental conditions in terms of the angle of the subject with respect to sensing and propose an ensemble approach for mitigating the effect of changing the sensing angle on the performance of the model. The last application that we address is the use of deep reinforcement learning to optimize the structure of the graphs in combinatorial optimization problems in logistics. Paper V introduces a general problem-independent hyperheuristic that utilizes the decision-making capability of deep reinforcement learning using a problem-independent state feature information. The proposed framework is trained on a general reward function to achieve state-of-the-art performance among popular solvers in the field of combinatorial optimization. We evaluate the performance of the proposed approach on three example routing problems as well as a scheduling problem to showcase the effectiveness of our method in different problems.Doktorgradsavhandlin

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