37 research outputs found
Toward Automating EA Configuration: The Parent Selection Stage
One of the obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers, is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. Having a mechanism for automatically configuring parameters and operators of every stage of the evolutionary life-cycle would give EAs a more widely spread popularity in the non-expert community. This paper investigates automatic configuration of one of the stages of the evolutionary life-cycle, the parent selection, via a new concept of semi-autonomous parent selection, where mate selection operators are encoded and evolved as in Genetic Programming. We compare the performance of the EA with semi-autonomous parent selection to that of a manually configured EA on three common test problems to determine the “price” we pay for user-friendliness
A New Adaptive Hungarian Mating Scheme in Genetic Algorithms
In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
유전 알고리즘에서의 적응적 짝짓기 제도
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 문병로.짝짓기 제도는 자식 해를 만들기 위하여 두 부모를 선택하는 방법을
말한다. 이는 유전 알고리즘의 동작 전반에 영향을 끼친다. 본 논문에서
는,헝가리안방법을사용한짝짓기제도에대해연구하였다.그제도들은
대응되는 거리의 합을 최소화하는 방법, 최대화하는 방법, 그리고 비교를
위해 랜덤하게 대응시키는 방법들을 가리킨다. 본 논문에서는 이 제도들
을잘알려진문제인순회판매원문제와그래프분할문제에적용하였다.
또한 세대별로 가장 좋은 해가 어떻게 변화하는지 분석하였다. 이러한 분
석에 기초하여, 본 논문에서는 간단히 결합된 짝짓기 제도를 제안하였다.
제안된 제도는 결합되지 않은 제도에 비해 더 좋은 결과를 보였다.
본 논문에서는 또한, 본 논문의 핵심 방법인 짝짓기 제도를 결합하는
방법을 제안한다. 본 논문의 적응적인 짝짓기 방법은 세 헝가리안 제도
중하나를선택한다.모든짝지어진쌍은다음세대를위한짝짓기방법을
결정할 투표권을 갖게 된다. 각각의 선호도는 부모해간 거리와 부모해와
자식해의 거리의 비율을 통해 결정된다. 제안된 적응적 방법은 모든 단일
헝가리안짝짓기제도,비적응적으로결합된방법,전통적인룰렛휠선택,
기존의다른거리기준방법들보다좋은결과를보였다.제안된적응적방
법은정기적인해집단의유입과지역최적화와결합된환경에서도적절한
제도를 선택했다. 본 논문에서는 헝가리안 방법을 최대 혹은 최소의 지역
최적점을찾는방법으로교체했다.이방식역시지역최적점을찾는단일
방법들보다 좋은 결과를 보였다I. Introduction 1
1.1 Motivation 1
1.2 Related Work 2
1.3 Contribution 4
1.4 Organization 6
II. Preliminary 7
2.1 Hungarian Method 7
2.2 Geometric Operators 10
2.2.1 Formal Definitions 10
2.3 Exploration Versus Exploitation Trade-off 11
2.4 Test Problems and Distance Metric 13
III. Hungarian Mating Scheme 15
3.1 Proposed Scheme 15
3.2 Tested GA 18
3.3 Observation 18
3.3.1 Traveling Salesman Problem 18
3.3.2 Graph Bisection Problem 21
IV. Hybrid and Adaptive Scheme 28
4.1 Simple Hybrid Scheme 28
4.2 Adaptive Scheme 30
4.2.1 Significance of Adaptive Scheme 30
4.2.2 Proposed Method 31
4.2.3 Theoretical Support 34
4.2.4 Experiments 36
4.2.5 Traveling Salesman Problem 36
4.2.6 Graph Bisection Problem 40
4.2.7 Comparison with Traditional Method 41
4.2.8 Comparison with Distance-based Methods 42
V. Tests in Various Environments 50
5.1 Hybrid GA 50
5.1.1 Experiment Settings 50
5.1.2 Results and Discussions 51
5.2 GA with New Individuals 52
5.2.1 Experiment Settings 52
5.2.2 Results and Discussions 53
VI. A Revised Version of Adaptive Method 62
6.1 Hungarian Mating Scheme 62
6.2 Experiment Settings 62
6.3 Results and Discussions 63
VII. Conclusion 67
7.1 Summary 67
7.2 Future Work 68Docto
Complex Systems: Nonlinearity and Structural Complexity in spatially extended and discrete systems
Resumen Esta Tesis doctoral aborda el estudio de sistemas de muchos elementos (sistemas discretos) interactuantes. La fenomenología presente en estos sistemas esta dada por la presencia de dos ingredientes fundamentales: (i) Complejidad dinámica: Las ecuaciones del movimiento que rigen la evolución de los constituyentes son no lineales de manera que raramente podremos encontrar soluciones analíticas. En el espacio de fases de estos sistemas pueden coexistir diferentes tipos de trayectorias dinámicas (multiestabilidad) y su topología puede variar enormemente dependiendo de dos parámetros usados en las ecuaciones. La conjunción de dinámica no lineal y sistemas de muchos grados de libertad (como los que aquí se estudian) da lugar a propiedades emergentes como la existencia de soluciones localizadas en el espacio, sincronización, caos espacio-temporal, formación de patrones, etc... (ii) Complejidad estructural: Se refiere a la existencia de un alto grado de aleatoriedad en el patrón de las interacciones entre los componentes. En la mayoría de los sistemas estudiados esta aleatoriedad se presenta de forma que la descripción de la influencia del entorno sobre un único elemento del sistema no puede describirse mediante una aproximación de campo medio. El estudio de estos dos ingredientes en sistemas extendidos se realizará de forma separada (Partes I y II de esta Tesis) y conjunta (Parte III). Si bien en los dos primeros casos la fenomenología introducida por cada fuente de complejidad viene siendo objeto de amplios estudios independientes a lo largo de los últimos años, la conjunción de ambas da lugar a un campo abierto y enormemente prometedor, donde la interdisciplinariedad concerniente a los campos de aplicación implica un amplio esfuerzo de diversas comunidades científicas. En particular, este es el caso del estudio de la dinámica en sistemas biológicos cuyo análisis es difícil de abordar con técnicas exclusivas de la Bioquímica, la Física Estadística o la Física Matemática. En definitiva, el objetivo marcado en esta Tesis es estudiar por separado dos fuentes de complejidad inherentes a muchos sistemas de interés para, finalmente, estar en disposición de atacar con nuevas perspectivas problemas relevantes para la Física de procesos celulares, la Neurociencia, Dinámica Evolutiva, etc..
Analysing functional genomics data using novel ensemble, consensus and data fusion techniques
Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge.
Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness.
The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results:
(1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining)
(2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre)
(3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs)
(4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre)
In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings