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Profiling, Whatif Analysis, and Costbased Optimization of MapReduce Programs
"... MapReduce has emerged as a viable competitor to database systems in big data analytics. MapReduce programs are being written for a wide variety of application domains including business data processing, text analysis, natural language processing, Web graph and social network analysis, and computatio ..."
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MapReduce has emerged as a viable competitor to database systems in big data analytics. MapReduce programs are being written for a wide variety of application domains including business data processing, text analysis, natural language processing, Web graph and social network analysis, and computational science. However, MapReduce systems lack a feature that has been key to the historical success of database systems, namely, costbased optimization. A major challenge here is that, to the MapReduce system, a program consists of blackbox map and reduce functions written in some programming language like C++, Java, Python, or Ruby. We introduce, to our knowledge, the first Costbased Optimizer for simple to arbitrarily complex MapReduce programs. We focus on the optimization opportunities presented by the large space of configuration parameters for these programs. We also introduce a Profiler to collect detailed statistical information from unmodified MapReduce programs, and a Whatif Engine for finegrained cost estimation. All components have been prototyped for the popular Hadoop MapReduce system. The effectiveness of each component is demonstrated through a comprehensive evaluation using representative MapReduce programs from various application domains. 1.
Benchmark functions for the cec’2010 special session and competition on largescale global optimization
 Nature Inspired Computation and Applications Laboratory
, 2009
"... In the past decades, different kinds of metaheuristic optimization algorithms [1, 2] have been developed; Simulated ..."
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In the past decades, different kinds of metaheuristic optimization algorithms [1, 2] have been developed; Simulated
Evolving distributed algorithms with genetic programming
 IEEE Transactions on Evolutionary Computation
, 2012
"... Abstract—In this article, we evaluate the applicability of Genetic Programming (GP) for the evolution of distributed algorithms. We carry out a largescale experimental study in which we tackle three wellknown problems from distributed computing with six different program representations. For this ..."
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Abstract—In this article, we evaluate the applicability of Genetic Programming (GP) for the evolution of distributed algorithms. We carry out a largescale experimental study in which we tackle three wellknown problems from distributed computing with six different program representations. For this purpose, we first define a simulation environment in which phenomena such as asynchronous computation at changing speed and messages taking over each other, i.e., outoforder message delivery, occur with high probability. Second, we define extensions and adaptations of established GP approaches (such as treebased and Linear Genetic Programming) in order to make them suitable for representing distributed algorithms. Third, we introduce novel rulebased Genetic Programming methods designed especially with the characteristic difficulties of evolving algorithms (such as epistasis) in mind. Based on our extensive experimental study of these approaches, we conclude that GP is indeed a viable method for evolving nontrivial, deterministic, nonapproximative distributed algorithms. Furthermore, one of the two rulebased approaches is shown to exhibit superior performance in most of the tasks and thus can be considered as an interesting idea also for other problem domains.
Rulebased Genetic Programming
 In Proceedings of BIONETICS 2007, 2nd International Conference on BioInspired Models of Network, Information, and Computing Systems
, 2007
"... In this paper we introduce a new approach for Genetic Programming, called rulebased Genetic Programming, or RBGP in short. A program evolved in the RBGP syntax is a list of rules. Each rule consists of two conditions, combined with a logical operator, and an action part. Such rules are independent ..."
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In this paper we introduce a new approach for Genetic Programming, called rulebased Genetic Programming, or RBGP in short. A program evolved in the RBGP syntax is a list of rules. Each rule consists of two conditions, combined with a logical operator, and an action part. Such rules are independent from each other in terms of position (mostly) and cardinality (always). This reduces the epistasis drastically and hence, the genetic reproduction operations are much more likely to produce good results than in other Genetic Programming methodologies. In order to verify the utility of our idea, we apply RBGP to a hard problem in distributed systems. With it, we are able to obtain emergent algorithms for mutual exclusion at a distributed critical section.
Evolutionary Freight Transportation Planning
 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
, 2009
"... Abstract. In this paper, we present the freight transportation planning component of the INWEST project. This system utilizes an evolutionary algorithm with intelligent search operations in order to achieve a high utilization of resources and a minimization of the distance travelled by freight carri ..."
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Abstract. In this paper, we present the freight transportation planning component of the INWEST project. This system utilizes an evolutionary algorithm with intelligent search operations in order to achieve a high utilization of resources and a minimization of the distance travelled by freight carriers in realworld scenarios. We test our planner rigorously with realworld data and obtain substantial improvements when compared to the original freight plans. Additionally, different settings for the evolutionary algorithm are studied with further experiments and their utility is verified with statistical tests. Preview This document is a preview version and not necessarily identical with the original.
Genetic Algorithm – an Approach to Solve Global Optimization
 Problems”, Indian Journal of Computer Science and Engineering
"... The genetic algorithm (GA) is a search heuristic that is routinely used to generate useful solutions to optimization and search problems. It generates solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Genetic a ..."
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The genetic algorithm (GA) is a search heuristic that is routinely used to generate useful solutions to optimization and search problems. It generates solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Genetic algorithms are one of the best ways to solve a problem for which little is known. They are a very general algorithm and so work well in any search space. All you need to know is what you need the solution to be able to do well, and a genetic algorithm will be able to create a high quality solution.
Solving RealWorld Vehicle Routing Problems with Evolutionary Algorithms
 Natural Intelligence for Scheduling, Planning and Packing Problems, volume 250 of Studies in Computational Intelligence, chapter 2
, 2009
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Thisdocument is a preview version and not necessarily identical with the original.
Novel Loop Structures and the Evolution of Mathematical Algorithms
 in Proc. of the 14th European Conference on Genetic Programming (EuroGP’11), ser. Lecture Notes in Computer Science
, 2011
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Cited by 4 (3 self)
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Offline Emergence Engineering For Agent Societies
"... Abstract. Many examples for emergent behaviors may be observed in selforganizing physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not c ..."
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Abstract. Many examples for emergent behaviors may be observed in selforganizing physical and biological systems which prove to be robust, stable, and adaptable. Such behaviors are often based on very simple mechanisms and rules, but artificially creating them is a challenging task which does not comply with traditional software engineering. In this article, we propose a hybrid approach by combining strategies from Genetic Programming and agent software engineering, and demonstrate that this
Geometric generalization of the neldermead algorithm
 In Proceedings of the 10th European Conference on Evolutionary Computation in Combinatorial Optimization
, 2010
"... Abstract. The NelderMead Algorithm (NMA) is an almost halfcentury old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). Geometric Particle Swarm Optimization (GPSO) and Geometric Differential Evolution (GDE) are rece ..."
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Abstract. The NelderMead Algorithm (NMA) is an almost halfcentury old method for numerical optimization, and it is a close relative of Particle Swarm Optimization (PSO) and Differential Evolution (DE). Geometric Particle Swarm Optimization (GPSO) and Geometric Differential Evolution (GDE) are recently introduced formal generalization of traditional PSO and DE that apply naturally to both continuous and combinatorial spaces. In this paper, we generalize NMA to combinatorial search spaces by naturally extending its geometric interpretation to these spaces, analogously as what was done for the traditional PSO and DE algorithms, obtaining the Geometric NelderMead Algorithm (GNMA). 1