scholarly journals Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework

2020 ◽  
Vol 34 (04) ◽  
pp. 3773-3780 ◽  
Author(s):  
Aryan Deshwal ◽  
Syrine Belakaria ◽  
Janardhan Rao Doppa ◽  
Alan Fern
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Black Box ◽  
Combinatorial Structures ◽  
High Performing ◽  
New Learning ◽  
Local Search Procedure ◽  
Complex Optimization ◽  
Control Knowledge ◽  
Discrete Spaces

We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial structures to evaluate, in order to identify high-performing structures as quickly as possible. Our main contribution is to introduce and evaluate a new learning-to-search framework for this problem called L2S-DISCO. The key insight is to employ search procedures guided by control knowledge at each step to select the next structure and to improve the control knowledge as new function evaluations are observed. We provide a concrete instantiation of L2S-DISCO for local search procedure and empirically evaluate it on diverse real-world benchmarks. Results show the efficacy of L2S-DISCO over state-of-the-art algorithms in solving complex optimization problems.

An Overview of the Last Advances and Applications of Greedy Randomized Adaptive Search Procedure

2018 ◽  
pp. 264-284 ◽  
Author(s):  
Airam Expósito Márquez ◽  
Christopher Expósito-Izquierdo
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Approximate Solutions ◽  
Search Procedure ◽  
Adaptive Search ◽  
Wide Range ◽  
Local Search Procedure ◽  
Complex Optimization

One of the most studied methods to get approximate solutions in optimization problems are the heuristics methods. Heuristics are usually employed to find good, but not necessarily optima solutions. The primary purpose of the chapter at hand is to provide a survey of the Greedy Randomized Adaptive Search Procedures (GRASP). GRASP is an iterative multi-start metaheuristic for solving complex optimization problems. Each GRASP iteration consists of a construction phase followed by a local search procedure. In this paper, we first describe the basic components of GRASP and the various elements that compose it. We present different variations of the basic GRASP in order to improve its performance. The GRASP has encompassed a wide range of applications, covering different fields because of its robustness and easy to apply.

scholarly journals Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Fu ◽  
Wangsheng Liu ◽  
Bin Zhang ◽  
Hua Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Complex Optimization

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

scholarly journals Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning

2019 ◽  
Vol 27 (1) ◽  
pp. 99-127 ◽  
Author(s):  
Pascal Kerschke ◽  
Heike Trautmann
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Ensemble Methods ◽  
Black Box ◽  
Small Sample ◽  
Landscape Analysis ◽  
Initial Population ◽  
Algorithm Selection ◽  
Practical Applications ◽  
High Performing

In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

scholarly journals Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators

2017 ◽  
Author(s):  
Guillem Francès ◽  
Miquel Ramírez ◽  
Nir Lipovetzky ◽  
Hector Geffner
Keyword(s):  
State Of The Art ◽  
Black Box ◽  
Initial State ◽  
Planning Algorithms ◽  
Control Knowledge ◽  
Domain Independent ◽  
Action Structure

Classical planning is concerned with problems where a goal needs to be reached from a known initial state by doing actions with deterministic, known effects. Classical planners, however, deal only with classical problems that can be expressed in declarative planning languages such as STRIPS or PDDL. This prevents their use on problems that are not easy to model declaratively or whose dynamics are given via simulations. Simulators do not provide a declarative representation of actions, but simply return successor states. The question we address in this paper is: can a planner that has access to the structure of states and goals only, approach the performance of planners that also have access to the structure of actions expressed in PDDL? To answer this, we develop domain-independent, black box planning algorithms that completely ignore action structure, and show that they match the performance of state-of-the-art classical planners on the standard planning benchmarks. Effective black box algorithms open up new possibilities for modeling and for expressing control knowledge, which we also illustrate.

scholarly journals Migration-Based Moth-Flame Optimization Algorithm

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2276
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Ali Fatahi ◽  
Hoda Zamani ◽  
Seyedali Mirjalili ◽  
Laith Abualigah ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Population Diversity ◽  
Local Optimum ◽  
Migration Strategy ◽  
High Performing ◽  
Different Dimensions ◽  
Complex Optimization ◽  
Benchmark Suite ◽  
Moth Flame Optimization Algorithm ◽  
Swarm Intelligence Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

scholarly journals NetXplain: Real-time explainability of graph neural networks applied to networking

2021 ◽  
Vol 2 (4) ◽  
pp. 57-66
Author(s):  
David Pujol-Perich ◽  
Jos� Su�rez-Varela ◽  
Shihan Xiao ◽  
Bo Wu ◽  
Albert Cabellos-Aparicio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Network Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Data Networks ◽  
Black Boxes ◽  
Complex Optimization ◽  
Graph Neural Networks ◽  
Qos Metrics

Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle complex optimization problems. However, existing DL-based solutions are often considered as black boxes with high inner complexity. As a result, there is still certain skepticism among the networking industry about their practical viability to operate data networks. In this context, explainability techniques have recently emerged to unveil why DL models make each decision. This paper focuses on the explainability of Graph Neural Networks (GNNs) applied to networking. GNNs are a novel DL family with unique properties to generalize over graphs. As a result, they have shown unprecedented performance to solve complex network optimization problems. This paper presents NetXplain, a novel real-time explainability solution that uses a GNN to interpret the output produced by another GNN. In the evaluation, we apply the proposed explainability method to RouteNet, a GNN model that predicts end-to-end QoS metrics in networks. We show that NetXplain operates more than 3 orders of magnitude faster than state-of-the-art explainability solutions when applied to networks up to 24 nodes, which makes it compatible with real-time applications; while demonstrating strong capabilities to generalize to network scenarios not seen during training.

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

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