Saddles and Barrier in Landscapes of Generalized Search Operators

Barrier trees are a convenient way of representing the structure of complex combinatorial landscapes over graphs. Here we generalize the concept of barrier trees to landscapes defined over general multi-parent search operators based on a suitable notion of topological connectedness that depends explicitly on the search operator. We show that in the case of recombination spaces, path-connectedness coincides with connectedness as defined by the mutation operator alone. In contrast, topological connectedness is more general and depends on the details of the recombination operators as well. Barrier trees can be meaningfully defined for both concepts of connectedness

Automated design of genetic programming of classification algorithms.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Over the past decades, there has been an increase in the use of evolutionary algorithms (EAs) for data mining and knowledge discovery in a wide range of application domains. Data classification, a real-world application problem is one of the areas EAs have been widely applied. Data classification has been extensively researched resulting in the development of a number of EA based classification algorithms. Genetic programming (GP) in particular has been shown to be one of the most effective EAs at inducing classifiers. It is widely accepted that the effectiveness of a parameterised algorithm like GP depends on its configuration. Currently, the design of GP classification algorithms is predominantly performed manually. Manual design follows an iterative trial and error approach which has been shown to be a menial, non-trivial time-consuming task that has a number of vulnerabilities. The research presented in this thesis is part of a large-scale initiative by the machine learning community to automate the design of machine learning techniques. The study investigates the hypothesis that automating the design of GP classification algorithms for data classification can still lead to the induction of effective classifiers. This research proposes using two evolutionary algorithms,namely,ageneticalgorithm(GA)andgrammaticalevolution(GE)toautomatethe design of GP classification algorithms. The proof-by-demonstration research methodology is used in the study to achieve the set out objectives. To that end two systems namely, a genetic algorithm system and a grammatical evolution system were implemented for automating the design of GP classification algorithms. The classification performance of the automated designed GP classifiers, i.e., GA designed GP classifiers and GE designed GP classifiers were compared to manually designed GP classifiers on real-world binary class and multiclass classification problems. The evaluation was performed on multiple domain problems obtained from the UCI machine learning repository and on two specific domains, cybersecurity and financial forecasting. The automated designed classifiers were found to outperform the manually designed GP classifiers on all the problems considered in this study. GP classifiers evolved by GE were found to be suitable for classifying binary classification problems while those evolved by a GA were found to be suitable for multiclass classification problems. Furthermore, the automated design time was found to be less than manual design time. Fitness landscape analysis of the design spaces searched by a GA and GE were carried out on all the class of problems considered in this study. Grammatical evolution found the search to be smoother on binary classification problems while the GA found multiclass problems to be less rugged than binary class problems

A tutorial for competent memetic algorithms: Model, taxonomy and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs

Problem dependent metaheuristic performance in Bayesian network structure learning.

Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven the use of heuristic algorithms for solving this kind of problem. Amajor recent focus in BN structure learning is on search and score algorithms. In these algorithms, a scoring function is introduced and a heuristic search algorithm is used to evaluate each network with respect to the training data. The optimal network is produced according to the best score evaluated. This thesis investigates a range of search and score algorithms to understand the relationship between technique performance and structure features of the problems. The main contributions of this thesis include (a) Two novel Ant Colony Optimization based search and score algorithms for BN structure learning; (b) Node juxtaposition distribution for studying the relationship between the best node ordering and the optimal BN structure; (c) Fitness landscape analysis for investigating the di erent performances of both chain score function and the CH score function; (d) A classifier method is constructed by utilizing receiver operating characteristic curve with the results on fitness landscape analysis; and finally (e) a selective o -line hyperheuristic algorithm is built for unseen BN structure learning with search and score algorithms. In this thesis, we also construct a new algorithm for producing BN benchmark structures and apply our novel approaches to a range of benchmark problems and real world problem

Analysis of combinatorial search spaces for a class of NP-hard problems, An

2011 Spring.Includes bibliographical references.Given a finite but very large set of states X and a real-valued objective function ƒ defined on X, combinatorial optimization refers to the problem of finding elements of X that maximize (or minimize) ƒ. Many combinatorial search algorithms employ some perturbation operator to hill-climb in the search space. Such perturbative local search algorithms are state of the art for many classes of NP-hard combinatorial optimization problems such as maximum k-satisfiability, scheduling, and problems of graph theory. In this thesis we analyze combinatorial search spaces by expanding the objective function into a (sparse) series of basis functions. While most analyses of the distribution of function values in the search space must rely on empirical sampling, the basis function expansion allows us to directly study the distribution of function values across regions of states for combinatorial problems without the need for sampling. We concentrate on objective functions that can be expressed as bounded pseudo-Boolean functions which are NP-hard to solve in general. We use the basis expansion to construct a polynomial-time algorithm for exactly computing constant-degree moments of the objective function ƒ over arbitrarily large regions of the search space. On functions with restricted codomains, these moments are related to the true distribution by a system of linear equations. Given low moments supplied by our algorithm, we construct bounds of the true distribution of ƒ over regions of the space using a linear programming approach. A straightforward relaxation allows us to efficiently approximate the distribution and hence quickly estimate the count of states in a given region that have certain values under the objective function. The analysis is also useful for characterizing properties of specific combinatorial problems. For instance, by connecting search space analysis to the theory of inapproximability, we prove that the bound specified by Grover's maximum principle for the Max-Ek-Lin-2 problem is sharp. Moreover, we use the framework to prove certain configurations are forbidden in regions of the Max-3-Sat search space, supplying the first theoretical confirmation of empirical results by others. Finally, we show that theoretical results can be used to drive the design of algorithms in a principled manner by using the search space analysis developed in this thesis in algorithmic applications. First, information obtained from our moment retrieving algorithm can be used to direct a hill-climbing search across plateaus in the Max-k-Sat search space. Second, the analysis can be used to control the mutation rate on a (1+1) evolutionary algorithm on bounded pseudo-Boolean functions so that the offspring of each search point is maximized in expectation. For these applications, knowledge of the search space structure supplied by the analysis translates to significant gains in the performance of search

Survivable virtual topology design in optical WDM networks using nature-inspired algorithms

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, 2012Thesis (PhD) -- İstanbul Technical University, Institute of Informatics, 2012Günümüzde bilgisayar ağları hayatımızın önemli bir parçası ve ihtiyaç haline gelmiştir. İstediğimiz veriye, istediğimiz anda, daha hızlı, daha güvenli ve kesintisiz olarak erişme isteğimiz aslında ağ altyapısının nasıl tasarlanacağını belirlemektedir. Kullanıcıların istekleri sürekli artarken, teknolojik gelişmelerle birlikte yeni yöntem ve algoritmalarla bu istekleri karşılamanın yolları aranmaktadır. Ağdaki aktarım hızı, aktarım ortamından doğrudan etkilenmektedir; bugün uzak mesafelere en yüksek kapasiteli ve hızlı aktarımın yapılabileceği ortam ise fiberdir. Fiber optik ağlar, fiberin üstün özelliklerini (hız, düşük bit hata oranı, elektromanyetik ortamlardan etkilenmeme, düşük işaret zayıflaması, fiziksel dayanıklılık, ucuzluk, güvenlilik, vs.) en iyi kullanacak şekilde tasarlanan ağlardır. Günümüzde dünyadaki iletişim ağ altyapısı, omurga ağlardan erişim ağlarına kadar, hızla fiber optik ağlara dönüşmektedir. Optik ağların en önemli özelliklerinden biri veri aktarım hızıdır, tek bir fiberden teorik olarak 50 Tb/s veri aktarımı yapılabileceği hesaplanmaktadır. Bugün, lider iletişim firmaları 100 Gb/s ya da 1 Tb/s hızda veri aktarımı yapacak kanalllardan bahsedebiliyorsa, bu, fiziksel altyapı optik bir omurgadan oluştuğu içindir. Dalgaboyu bölmeli çoğullama (WDM) teknolojisi sayesinde bir fiber üzerinde aynı anda kurulabilecek kanal sayısı, günümüz teknolojisiyle yüzler mertebesine çıkabilmektedir. Dalgaboyu bölmeli çoğullama teknolojisi ile, optik aktarım birbiriyle çakışmayan dalgaboyu bantlarına bölünür ve her bir dalgaboyu istenen hızda çalışan, ışıkyolu olarak adlandırılan, bir iletişim kanalını destekler. Böylece, yakın gelecek için öngörülen çok yüksek hızlara çıkmadan bile, bir fiberden herbiri birkaç on Gb/s hızda çalışan yüz dolayında ışıkyolu geçebilmektedir. Bu kadar yüksek hızlarda veri aktarımı, özellikle her bir fiberinde çok sayıda kanalın taşındığı omurga ağlarda bir konuya büyük önem kazandırmaktadır: Hataya bağışıklık. En sık rastlanan hata olan, bir fiberin, herhangi bir nedenle kesilmesi (çoğunlukla inşaat makineleri tarafından, ya da doğal afetlerce), fiber tamir edilene kadar, her saniyede birkaç terabitlik veri kaybı anlamına gelecektir. Örnek olarak 10 km uzunlukta bir fiberin kopma sıklığı 11 yılda birdir. Omurga ağlarda yüzlerce, bazen binlerce, kilometrelik fiberler döşendiği gözönüne alındığında, böyle bir hata durumu için tedbir alınmaması düşünülemez. Optik ağ üzerindeki herhangi bir fibere zarar gelmesi demek bu fiber üzerinden yönlendirilmiş olan tüm ışıkyollarının kopması demektir. Her bir ışıkyolu üzerinden yüksek miktarda (40 Gb/s) veri aktarımı yapıldığından, böyle bir zarar ciddi veri kayıplarına neden olabilir. Temel olarak fiber kopmasına karşı geliştirilen iki yaklaşım vardır. Birinci yaklaşımda fiber üzerinden geçen her bir bağlantının, yani ışıkyolunun, yedek yollarla korunmasıdır. İkinci yaklaşım ise, özellikle birçok internet uygulamasına da uygun ve yeterli olacak şekilde, ışıkyollarının oluşturduğu sanal topolojinin bağlı kalmasının sağlanmasıdır. Bu ikinci yaklaşımda herbir ışıkyoluna ayrı ayrı yedek koruma yollarının atanması yerine, sanal topolojinin korunması dikkate alınarak, üst katmanların (paket katmanları) koruma mekanizmalarının devreye girebilmesi için gereken minimum koşulların sağlanması amaçlanmaktadır. Birinci yaklaşım belirli düzeylerde garantili bir koruma sağlarken yüksek miktarda ağ kaynağının atıl durmasına neden olmakta, dolayısıyla bu kadar üst düzey koruma gerektirmeyen uygulamalar için pahalı bir çözüm sunmaktadır. Son yıllarda özellikle dikkat çeken ikinci yaklaşım ise, daha ekonomik bir yöntemle iletişimin kopmaması garantisini vermekte, ancak daha yavaş bir düzeltme sağlamaktadır. Günümüzde birçok uygulama bağlantı kopmadığı sürece paket katmanının, yeni yol bulma gibi hata düzeltme mekanizmalarının devreye girmesi için gerekli olan, dakikalar mertebesindeki gecikmelere toleranslıdır (web dolaşımı, dosya aktarımı, mesajlaşma, uzaktan erişim gibi). Bu yaklaşım ilkine göre daha az ağ kaynağının atıl kalmasına neden olarak kullanıcıya daha ekonomik hizmet verilmesini sağlayacaktır. Bu çalışmada üzerinde durduğumuz hataya bağışık sanal topoloji tasarımı problemi de bu ikinci yaklaşımı benimsemektedir. Hataya bağışık sanal topoloji tasarımı problemi kendi içinde dört alt probleme ayrılmaktadır: ışıkyollarının belirlenmesi (sanal topolojiyi oluşturma), bu ışıkyollarının herhangi bir fiber kopması durumunda bile sanal topolojinin bağlı kalmasını sağlayacak sekilde fiziksel topoloji üzerinde yönlendirilmesi, dalgaboyu atanması, ve paket trafiğinin yönlendirilmesi. Bu alt problemler ayrı ayrı çözülebilir. Ancak, bunlar bağımsız problemler değildir ve bunları tek tek çözmek elde edilen çözümün kalitesinin çok düşük olmasına neden olabilir. Bununla birlikte, hataya bağışık sanal topoloji tasarımı problemi NP-karmaşıktır. Karmaşıklığı nedeniyle bu problemin, gerçek boyutlu ağlar için, klasik optimizasyon teknikleriyle kabul edilebilir zamanda çözülmesi mümkün değildir. Bu çalışmada, fiziksel topolojinin ve düğümler arası paket trafiği yoğunluğunun bilindiği durumlar için, hataya bağışık sanal topoloji tasarımı problemi bütün halinde ele alınmaktadır. Tezin ilk aşamasında, hataya bağışık sanal topoloji tasarımı probleminin alt problemi olan hataya bağışık sanal topoloji yönlendirmesi problemi ele alınmıştır. Verilen bir sanal topoloji için en az kaynak kullanarak hataya bağışık yönlendirme yapmak için iki farklı doğa-esinli algoritma önerilmektedir: evrimsel algoritmalar ve karınca kolonisi optimizasyonu. Öncelikle önerilen algoritmaların problem için uygun parametre kümesi belirlenmiş, daha sonra, algoritmaların başarımını ölçmek için, deneysel sonuçlar tamsayı doğrusal programlama (ILP) ile elde edilen sonuçlarla karşılaştırılmışır. Sonuçlar göstermektedir ki; önerdiğimiz iki algoritma da, tamsayı doğrusal programlama ile uygun bir çözüm bulunamayan büyük ölçekli ağlar için dahi, problemi çözebilmektedir. Bunun yanında, doğa-esinli algoritmalar çok daha az CPU zamanı ve hafıza kullanmaktadır. Elde edilen çözüm kalitesi ve çözüm için kullanılan CPU zamanının kabul edilebilir düzeyde olması, her iki doğa-esinli algoritmanın da gerçek boyutlu ağlar için kullanılabileceğini doğrulamaktadır. İkinci aşamada, hataya bağışık sanal topoloji tasarımı problemini bir bütün halinde çözmek için dört farklı üst-sezgisel yöntem önerilmektedir. Önerilen üst-sezgisel yöntemler alt seviyedeki sezgiselleri seçme asamasında dört farklı yöntem kullanmaktadır: evrimsel algoritmalar, benzetimli tavlama, karınca kolonisi optimizasyonu ve uyarlamalı yinelenen yapıcı arama. Deneysel sonuçlar tüm üst-sezgisel yöntemlerin hataya bağışık sanal topoloji tasarımı problemini çözmede başarılı olduğunu göstermektedir. Ancak, karınca kolonisi optimizasyonu tabanlı üst-sezgisel diğerlerine göre daha üstün sonuçlar vermektedir. Işıkyolları üzerindeki trafik akışını dengelemek için, karınca kolonisi optimizasyonu tabanlı üst-sezgisele akış deviasyonu yöntemi de eklenmiştir. Literatürde hataya bağışık sanal topoloji tasarımı problemini ele alan tüm çalışmalar çift fiber kopması durumunu gözardı etmektedir. Bu çalışmada, önerdiğimiz üst-sezgisel yöntemin başarımını hem tek hem de çift fiber kopması durumları için değerlendirdik. Önerdiğimiz yöntem çoklu fiber kopması durumları için çok kolay şekilde adapte edilebilmektedir. Tek yapılması gereken hataya bağışıklık kontrolünü yapan yordamın değiştirilmesidir. Deneysel sonuçlar göstermiştir ki, önerdiğimiz karınca kolonisi optimizasyonu tabanlı üst-sezgisel hataya bağışık sanal topoloji tasarımı problemini hem tek hem de çift fiber kopması durumları için kabul edilebilir bir sürede çözebilmektedir. Üst-sezgisel yöntemlerin hataya bağışık sanal topoloji tasarımı çözmedeki başarımını değerlendirebilmek amacıyla, karınca kolonisi optimizasyonu tabanlı üst-sezgiselle elde edilen sonuçlar, literatürde bu problem için önerilmiş başka bir yöntemle karşılaştırılmıştır. Sonuçlar üst-sezgisel yöntemlerin, çok daha az CPU zamanı kullanarak, problem için daha kaliteli çözümler verdiğini göstermektedir.Today, computer networking has become an integral part of our daily life. The steady increase in user demands of high speed and high bandwidth networks causes researchers to seek out new methods and algorithms to meet these demands. The transmission speed in the network is directly affected by the transmission medium. The most effective medium to transmit data is the fiber. Optical networks are designed for the best usage of the superior properties of the fiber, e.g. high speed, high bandwidth, low bit error rate, low attenuation, physical strength, cheapness, etc. The world's communication network infrastructure, from backbone networks to access networks, is consistently turning into optical networks. One of the most important properties of the optical networks is the data transmission rate (up to 50 Tb/s on a single fiber). Today, with the help of the wavelength division multiplexing (WDM) technology, hundreds of channels can be built on a single fiber. WDM is a technology in which the optical transmission is split into a number of non-overlapping wavelength bands, with each wavelength supporting a single communication channel operating at the desired rate. Since multiple WDM channels, also called lightpaths, can coexist on a single fiber, the huge fiber bandwidth can be utilized. Any damage to a physical link (fiber) on the network causes all the lightpaths routed through this link to be broken. Since huge data transmission (40 Gb/s) over each of these lightpaths is possible, such a damage results in a serious amount of data loss. Two different approaches can be used in order to avoid this situation: 1. Survivability on the physical layer, 2. Survivability on the virtual layer. The first approach is the problem of designing a backup link/path for each link/path of the optical layer. The second approach is the problem of designing the optical layer such that the optical layer remains connected in the event of a single or multiple link failure. While the first approach provides faster protection for time-critical applications (such as, IP phone, telemedicine) by reserving more resources, the second approach, i.e. the survivable virtual topology design, which has attracted a lot of attention in recent years, aims to protect connections using less resources. The problem that will be studied in this project is to develop methods for survivable virtual topology design, that enables effective usage of the resources. Survivable virtual topology design consists of four subproblems: determining a set of lightpaths (forming the virtual topology), routing these lightpaths on the physical topology (routing and wavelength assignment (RWA) problem), so that any single fiber cut does not disconnect the virtual topology (survivable virtual topology mapping), assigning wavelengths, and routing the packet traffic. Each of these subproblems can be solved separately. However, they are not independent problems and solving them one by one may degrade the quality of the final result considerably. Furthermore, the survivable virtual topology design is known to be NP-complete. Because of its complexity, it is not possible to solve the problem optimally in an acceptable amount of time using classical optimization techniques, for real-life sized networks. In this thesis, we solve the survivable virtual topology design problem as a whole, where the physical topology and the packet traffic intensities between nodes are given. In the first phase, we propose two different nature inspired heuristics to find a survivable mapping of a given virtual topology with minimum resource usage. Evolutionary algorithms and ant colony optimization algorithms are applied to the problem. To assess the performance of the proposed algorithms, we compare the experimental results with those obtained through integer linear programming. The results show that both of our algorithms can solve the problem even for large-scale network topologies for which a feasible solution cannot be found using integer linear programming. Moreover, the CPU time and the memory used by the nature inspired heuristics is much lower. In the second phase, we propose four different hyper-heuristic approaches to solve the survivable virtual topology design problem as a whole. Each hyper-heuristic approach is based on a different category of nature inspired heuristics: evolutionary algorithms, ant colony optimization, simulated annealing, and adaptive iterated constructive search. Experimental results show that, all proposed hyper-heuristic approaches are successful in designing survivable virtual topologies. Furthermore, the ant colony optimization based hyper-heuristic outperforms the others. To balance the traffic flow over lightpaths, we adapt a flow-deviation method to the ant colony optimization based hyper-heuristic approach. We explore the performance of our hyper-heuristic approach for both single and double-link failures. The proposed approach can be applied to the multiple-link failure problem instances by only changing the survivability control routine. The experimental results show that our approach can solve the problem for both single-link and double-link failures in a reasonable amount of time. To evaluate the quality of the HH approach solutions, we compare these results with the results obtained using tabu search approach. The results show that HH approach outperforms tabu search approach both in solution quality and CPU time.DoktoraPh

Anatomy of the Local Optima Level in Combinatorial Optimisation

Many situations in daily life represent complex combinatorial optimisation problems. These include issues such as efficient fuel consumption, nurse scheduling, or distribution of humanitarian aid. There are many algorithms that attempt to solve these problems but the ability to understand their likely performance on a given problem is still lacking. Fitness landscape analysis identifies some of the reasons why metaheuristic algorithms behave in a particular way. The Local Optima Network (LON) model, proposed in 2008, encodes local optima connectivity in fitness landscapes. In this approach, nodes are local optima and edges encode transitions between these optima. A LON provides a static image of the dynamics of algorithm-problem inter- play. Analysing these structures provides insights into the reactions between optimisation problems and metaheuristic search algorithms. This thesis proposes that analysis of the local optima space of combinatorial fitness landscapes encoded using a LON provides important information concerning potential search algorithm performance. It considers the question as to whether or not features of LONs can contribute to explaining or predicting the outcome of trying to optimise an associated combinatorial problem. Topological landscape features of LONs are proposed, analysed and compared. Benchmark and novel problem instances are studied; both types of problem are sampled and in some cases exhaustively-enumerated such that LONs can be extracted for analysis. Investigations into the nature and biases of LON construction algorithms are conducted and compared. Contributions include aligning fractal geometry to the study of LONs; proposals for novel ways to compute fractal dimension from these structures; comparing the power of different LON construction algorithms for explaining algorithm performances; and analysing the interplay between algorithmic operations and infeasible regions in the local optima space using LONs as a tool. Throughout the thesis, large scale structural patterns in fitness landscapes are shown to be strongly linked with metaheuristic algorithm performance. This includes arrangements of local optima funnel structures; spatial and geometric complexity in the LON (measured by their fractal dimensionality) and fitness levels in the space of local optima. These features are demonstrated to have explanatory or predictive ability with respect to algorithm performance for the underlying combinatorial problems. The results presented here indicate that large topological patterns in fitness landscapes are important during metaheuristic search algorithm design. In many cases they are incontrovertibly linked to the success of the algorithm. These results indicate that use of the suggested fitness landscape measures would be highly beneficial when considering the design of search algorithms for a given problem domain

Circuit Clustering for Cluster-based FPGAs Using Novel Multiobjective Genetic Algorithms

Circuit clustering is one of the most crucial steps in a post-synthesis FPGA CAD flow. It attempts to efficiently fit synthesised logic functions into FPGA logic clusters. On a FPGA, different clusterings result in different circuit mappings, which affect FPGA utilisation, routability and timing, and therefore impact the circuit performance. This research proposes the use of a Multi Objective Genetic Algorithm (MOGA) as a methodology to solve the cluster-based FPGA circuit clustering problem. Four alternative approaches based on MOGA methods are proposed in this research: RVPack is inspired by the stochastic feature that exists in Evolutionary Algorithms (EAs). GGAPack, GGAPack2, DBPack and HYPack, T-HYPack (Timing-driven HYPack) are then proposed and developed, which are fully customised MOGA-based circuit clustering methods. GGAPack clusters a circuit using a top-down perspective, and DBPack uses a new bottom-up perspective. HYPack combines GGAPack and HYPack -- a hybrid method. According to experimental results, a few conclusions are drawn: It is possible to improve the performance of the greedy algorithm based circuit clustering methods by incorporating randomness. The performance of MOGA based top-down clustering is poor; however, using MOGA to cluster a circuit from a bottom-up perspective can produce better solutions. T-HYPack clustered circuit has the best timing performance compared with state-of-the-art methods. The experimental results also reflect a wider potential for using GAs to solve FPGA circuit mapping problems

Efficient learning methods to tune algorithm parameters

This thesis focuses on the algorithm configuration problem. In particular, three efficient learning configurators are introduced to tune parameters offline. The first looks into metaoptimization, where the algorithm is expected to solve similar problem instances within varying computational budgets. Standard meta-optimization techniques have to be repeated whenever the available computational budget changes, as the parameters that work well for small budgets, may not be suitable for larger ones. The proposed Flexible Budget method can, in a single run, identify the best parameter setting for all possible computational budgets less than a specified maximum, without compromising solution quality. Hence, a lot of time is saved. This will be shown experimentally. The second regards Racing algorithms which often do not fully utilize the available computational budget to find the best parameter setting, as they may terminate whenever a single parameter remains in the race. The proposed Racing with reset can overcome this issue, and at the same time adapt Racing’s hyper-parameter α online. Experiments will show that such adaptation enables the algorithm to achieve significantly lower failure rates, compared to any fixed α set by the user. The third extends on Racing with reset by allowing it to utilize all the information gathered previously when it adapts α, it also permits Racing algorithms in general to intelligently allocate the budget in each iteration, as opposed to equally allocating it. All developed Racing algorithms are compared to two budget allocators from the Simulation Optimization literature, OCBA and CBA, and to equal allocation to demonstrate under which conditions each performs best in terms of minimizing the probability of incorrect selection