Is reduction in task space a condition for accelerated learning?

Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces. [Code] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> Codebase: https://github.com/castacks/tigris [Video] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> Video: https://youtu.be/bMw5nUGL5GQ

Search is considered to be an important functionality in a computational system. Search techniques are applied in file retrievals and indexing. Though there exists various search techniques, binary search is widely used in many applications due to its advantage over other search techniques namely linear and hash search. Binary search is easy to implement and is used to search for an element in a large search space. The worst case time complexity of binary search is O (log 2 n) where n is the number of elements (search space) in the array. However, in binary search, searching is performed on the entire search space. The complexity of binary search may be further reduced if the search space is reduced. This paper proposes an Ant Colony Optimization based Binary Search (ACOBS) algorithm to find an optimal search space for binary search. ACOBS algorithm categorizes the search space and the key element is searched only in a specific category where the key element can exist thereby reducing the search space. The time complexity of ACOBS algorithm is O (log 2 c) where c is the number of elements in the reduced search space and c < n. The proposal is best suited for real time applications where searching is performed on a large domain.KeywordsAnt colony optimizationBinary searchOptimal search space

Deep neural networks designed for point clouds, also called point cloud neural networks (PCNNs), are attracting increasing attention in recent years. In this work, we propose the first model compression framework referred to as JointPruning (JP) that is specifically designed for compressing PCNNs. Observing that the redundancies in PCNNs are largely affected by certain parameters like the number of points, we first propose a new search space specifically designed for PCNNs. By searching the optimal pruning policy in our newly proposed search space, our JP framework can prune the PCNNs at different levels and simultaneously reduce the redundancies along multiple dimensions. As the newly proposed search space consists of multiple levels and the policy value at each level is continuous in our JP framework, it is hard to directly search for the best pruning policy in such a large search space. To this end, we further propose two strategies called search space refinement and validation set extension to progressively refine the granularity of our searching process in a coarse-to-fine and easy-to-hard fashion, which can help us gradually find better pruning policies. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our JP framework for compressing PCNNs.

End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existing AutoML systems, however, suffer from scalability issues when applying to application domains with large, high-dimensional search spaces. We present VOLCANOML, a scalable and extensible framework that facilitates systematic exploration of large AutoML search spaces. VOLCANOML introduces and implements basic building blocks that decompose a large search space into smaller ones, and allows users to utilize these building blocks to compose an execution plan for the AutoML problem at hand. VOLCANOML further supports a Volcano-style execution model - akin to the one supported by modern database systems - to execute the plan constructed. Our evaluation demonstrates that, not only does VOLCANOML raise the level of expressiveness for search space decomposition in AutoML, it also leads to actual findings of decomposition strategies that are significantly more efficient than the ones employed by state-of-the-art AutoML systems such as auto-sklearn.

A heuristic approach to optimization of complex physicochemical processes in the form of a genetic algorithm for solving problems is presented. In comparison with other evolutionary methods, the genetic algorithm allows working with large search spaces and complex evaluation functions, which is especially important in the study of multifactor physicochemical systems. Due to the relatively high need for computing resources when working with large and complex search spaces, optimization of existing calculation organization schemes has a positive effect on the accuracy of the calculated results. The paper presents a modified genetic algorithm that minimizes the number of iterations to achieve a given accuracy when solving the problem of finding the optimal composition of the initial reaction mixture. For a complex physicochemical process, an optimization problem is formulated which consists in finding the composition of the initial reaction mixture that promotes maximization (or minimization) of a given target parameter. The optimality criterion is determined by the type of the problem being solved and, when organizing calculations, is focused on the maximum yield of the target product. The main steps of implementing the genetic algorithm include creating an initial set of solutions and subsequent iterative evaluation of their quality for subsequent combination and modification until optimal values are achieved using mechanisms similar to biological evolution. To improve the efficiency of the method and reduce the number of iterations, a modification of the genetic algorithm is proposed which boils down to a dynamic estimate of the “mutation” parameter, depending on the diversity of individuals in the formed population of solutions. In a series of computational experiments, an analysis was made of the influence of the genetic algorithm parameters on the accuracy and efficiency of solving the problem using the example of studying the kinetics of the Michaelis-Menten enzymatic reaction. The results of calculations to determine the optimal composition of the reaction mixture showed that the dynamic determination of the “mutation” parameter contributes to an increase in the accuracy of the problem solution and a multiple decrease in the relative error value reaching 0.77 % when performing 200 iterations and 0.21 % when performing 400 iterations. The presented modified approach to solving the optimization problem is not limited by the type and content of the studied physicochemical process. The calculations performed showed a high degree of influence of the “mutation” parameter on the accuracy and efficiency of the problem solution, and dynamic control of the value of this parameter allowed increasing the speed of the genetic algorithm and reduce the number of iterations to achieve an optimal solution of a given accuracy. This is especially relevant in the study of multifactorial systems when the influence of parameters is non-trivial.

PC-SSRDE: A paradigm crossover-based differential evolution algorithm with search space reduction

본 논문에서는 유전알고리즘의 유전자형-표현형 기법을 적용한 수정된 이진 입자군집최적화의 두 번째 버전을 소개한다. 입자군집최적화는 해를 탐색해 나가는 과정에서 주변의 우수한 해의 위치와 자신의 위치차이 정보를 이용한다. 이러한 위치 차이를 구하는데 있어서 첫 번째 버전의 수정된 이진 입자군집최적화는 표현형을 사용한 반면에 제안하는 버전은 유전자형을 사용한다. 이진 정보만을 제공하는 표현형에 비해 연속 공간 전체를 탐색공간으로 제공하는 유전자형 정보를 사용하여 해 공간을 보다 넓은 공간으로 표시할 수 있다. 벤치마크 함수인 10개의 De Jong 함수에 실험한 결과, 두 번째 버전은 탐색 공간이 넓고 지역 최적해가 많은 함수에서 첫 번째 버전에 보다 우수한 결과를 얻었다. In this paper, we introduce a second version of modified binary particle swarm optimization using a concept of genotype-phenotype in genetic algorithms. Particle swarm optimization uses an information of difference between a position of the best solution and one's own position in the process of searching optimum. To obtain this difference of positions, the first version of modified binary particle swarm optimization uses a phenotype but the proposed second version uses a genotype. We can represent the solution space in large search space by using a genotype which provides continuous whole space as search space compared to a phenotype which provides only binary information. Experimental results in 10 De Jong benchmark function show that the second version outperforms the first version in six functions which has a broad search space and many local optima.

In order to improve the relatively slow convergence of GA, in the presence of large search spaces, and reduce the high consuming time of evaluation functions in analog circuit design applications, this chapter will discuss the use of learning algorithms. These algorithms explore the successive generation of solutions, learn the tendency of the best optimization variables and will use this knowledge to predict future values. In other words, these techniques employ data mining theory, used to manage large databases and huge amount of internet information, to discover complex relationships among various factors and extract meaningful knowledge to improve the efficiency and quality of decision making. In this chapter a new hybrid optimization algorithm is presented together with a design methodology, which increases the efficiency on the analog circuit design cycle. This new algorithm combines an enhanced GA kernel with an automatic learning machine based on SVM model (GA-SVM) which efficiently guides the selection operator of the GA algorithm avoiding time-consuming SPICE evaluations of non-promising solutions. The SVM model is here defined as a classification model used to predict the feasibility region in the presence of large, non-linear and constraints search spaces that characterize analog design problems. The SVM modeling attempts to constraint the search space in order to accelerate the search towards the feasible region ensuring a proper operation of the circuit.KeywordsSupport Vector MachineDesign SpaceFeasibility RegionSupport Vector Machine ModelAnalog CircuitThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The synthesis of programmatic strategies requires one to search in large non-differentiable spaces of computer programs. Current search algorithms use self-play approaches to guide this search. The issue with these approaches is that the guiding function often provides a weak search signal. This is because self-play functions only measure how well a program performs against other programs. Thus, while small changes to a losing program might not transform it into a winning one, such changes might represent steps in the direction of a winning program. In this paper we introduce a bilevel search algorithm that searches concurrently in the space of programs and in a space of state features. Each iteration of the search in the space of features defines a set of target features that the search in the program space attempts to achieve (i.e., features one observes while following the strategy encoded in a program). We hypothesize the combination of a self-play function and a feature-based one provides a stronger search signal for synthesis. While both functions are used to guide the search in the program space, the self-play function is used to guide the search in the feature space, to allow for the selection of target features that are more likely to lead to winning programs. We evaluated our bilevel algorithm in MicroRTS, a real-time strategy game. Our results show that the bilevel search synthesizes stronger strategies than methods that search only in the program space. Also, the strategies our method synthesizes obtained the highest winning rate in a simulated tournament with several baseline agents, including the best agents from the two latest MicroRTS competitions.

Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, which make it is infeasible to search exhaustively. As a search strategy, genetic algorithms have been applied successfully in many fields. In this paper, we present a genetic algorithm-based approach for mining classification rules from large database. For emphasizing on predictive accuracy, comprehensibility and interestingness of the rules and simplifying the implementation of a genetic algorithm, we discuss detail the design of encoding, genetic operator and fitness function of genetic algorithm for this task. Experimental result shows that genetic algorithm proposed in this paper is suitable for classification rule mining and those rules discovered by the algorithm have higher classification performance to unknown data.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB® Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

Monte Carlo Tree Search (MCTS) is a novel machine learning paradigm that is used to find good solutions for complex optimization problems with very large search spaces (like playing GO). We combine MCTS with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to efficiently optimize real-parameter single objective problems by balancing the exploitation of promising areas with the exploration of new regions of the search space. The novel algorithm is called hierarchical CMA-ES and it is influenced by both machine learning and evolutionary computation research areas. Like in evolutionary computation, we use a population of individuals to explore the commonalities of CMA-ES solvers. These CMA-ES solvers are structured using a MCTS tree like structure. Our experiments compare the performance of hierarchical CMA-ES solvers with two other algorithms: the standard CMA-ES optimizer, and an adaptation of MCTS to solve real-parameter problems. The hierarchical CMA-ES optimizer has the best empirical performance on several benchmark problems.

This paper focuses on the performance evaluation of complex man-made systems, such as assembly lines, electric power grid, traffic systems, and various paper processing bureaucracies, etc. For such problems, applying the traditional optimization tool of mathematical programming and gradient descent procedures of continuous variables optimization are often inappropriate or infeasible, as the design variables are usually discrete and the accurate evaluation of the system performance via a simulation model can take too much calculation. General search type and heuristic methods are the only two methods to tackle the problems. However, the "goodness" of heuristic methods is generally difficult to quantify while search methods often involve extensive evaluation of systems at many design choices in a large search space using a simulation model resulting in an infeasible computation burden. The purpose of this paper is to address these difficulties simultaneously by extending the recently developed methodology of Ordinal Optimization (OO). Uniform samples are taken out from the whole search space and evaluated with a crude but computationally easy model when applying OO. And, we argue, after ordering via the crude performance estimates, that the lined-up uniform samples can be seen as an approximate ruler. By comparing the heuristic design with such a ruler, we can quantify the heuristic design, just as we measure the length of an object with a ruler. In a previous paper we showed how to quantify a heuristic design for a special case but we did not have the OO ruler idea at that time. In this paper we propose the OO ruler idea and extend the quantifying method to the general case and the multiple independent results case. Experimental results of applying the ruler are also given to illustrate the utility of this approach.

A new approach to regional land use and transportation planning, which uses a genetic algorithm as an integrated optimization tool, is presented. The approach is illustrated by applying it to the Wasatch Front Metropolitan Region, which consists of four counties in the state of Utah. This genetic algorithm–-based approach was applied earlier to the twin cities of Provo and Orem in Utah, but here it is adapted to regional planning. Three issues make regional planning particularly difficult: ( a) individual cities have significant planning autonomy, ( b) the search space of possible plans is immense, and ( c) preferences between competing objectives vary among stakeholders. The approach used here addresses the first issue by the way the problem is formulated. The second issue is addressed with a genetic algorithm. Such algorithms are particularly well suited to problems with large search spaces. The third issue is addressed by using a multiobjective fitness function in the genetic algorithm. It was found that a genetic algorithm could produce a set of nondominated future land use scenarios and street plans for a region, from which regional planners can make a selection. Execution of the algorithm to produce 100 plans per generation for 100 generations took about 4 days with a high-end personal computer. Interesting trends for reducing change and traffic congestion were discovered.

Finding good cloud configurations for deploying a single distributed system is already a challenging task, and it becomes substantially harder when a data analytics cluster is formed by multiple distributed systems since the search space becomes exponentially larger. In particular, recent proposals for single system deployments rely on benchmarking runs that become prohibitively expensive as we shift to joint optimization of multiple systems, as users have to wait until the end of a long optimization run to start the production run of their job. We propose Vanir, an optimization framework designed to operate in an ecosystem of multiple distributed systems forming an analytics cluster. To deal with this large search space, Vanir takes the approach of quickly finding a good enough configuration and then attempts to further optimize the configuration during production runs. This is achieved by combining a series of techniques in a novel way, namely a metrics-based optimizer for the benchmarking runs, and a Mondrian forest-based performance model and transfer learning during production runs. Our results show that Vanir can find deployments that perform comparably to the ones found by state-of-the-art single-system cloud configuration optimizers while spending 2X fewer benchmarking runs. This leads to an overall search cost that is 1.3 - 24X lower compared to the state-of-the-art. Additionally, when transfer learning can be used, Vanir can minimize the benchmarking runs even further, and use online optimization to achieve a performance comparable to the deployments found by today's single-system frameworks.