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Artificial Mutation inspired Hyper-heuristic for Runtime Usage of Multi-objective Algorithms  [PDF]
Donia El Kateb,Fran?ois Fouquet,Johann Bourcier,Yves Le Traon
Computer Science , 2014,
Abstract: In the last years, multi-objective evolutionary algorithms (MOEA) have been applied to different software engineering problems where many conflicting objectives have to be optimized simultaneously. In theory, evolutionary algorithms feature a nice property for runtime optimization as they can provide a solution in any execution time. In practice, based on a Darwinian inspired natural selection, these evolutionary algorithms produce many deadborn solutions whose computation results in a computational resources wastage: natural selection is naturally slow. In this paper, we reconsider this founding analogy to accelerate convergence of MOEA, by looking at modern biology studies: artificial selection has been used to achieve an anticipated specific purpose instead of only relying on crossover and natural selection (i.e., Muller et al [18] research on artificial mutation of fruits with X-Ray). Putting aside the analogy with natural selection , the present paper proposes an hyper-heuristic for MOEA algorithms named Sputnik 1 that uses artificial selective mutation to improve the convergence speed of MOEA. Sputnik leverages the past history of mutation efficiency to select the most relevant mutations to perform. We evaluate Sputnik on a cloud-reasoning engine, which drives on-demand provisioning while considering conflicting performance and cost objectives. We have conducted experiments to highlight the significant performance improvement of Sputnik in terms of resolution time.
True Global Optimality of the Pressure Vessel Design Problem: A Benchmark for Bio-Inspired Optimisation Algorithms  [PDF]
Xin-She Yang,Christian Huyck,Mehmet Karamanoglu,Nawaz Khan
Physics , 2014, DOI: 10.4018/jdsst.2013040103
Abstract: The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimization algorithms. However, its global optimality is not clear and there has been no mathematical proof put forward. In this paper, a detailed mathematical analysis of this problem is provided that proves that 6059.714335048436 is the global minimum. The Lagrange multiplier method is also used as an alternative proof and this method is extended to find the global optimum of a cantilever beam design problem.
Heuristic Function Optimization Inspired by Social Competitive Behaviors  [PDF]
Ali Borji
Journal of Applied Sciences , 2008,
Abstract: The aim of present study is to introduce a heuristic optimization method which is inspired from competitions in social behaviors. Competitive behaviors could be observed in large number of situations of human social life. Particularly we propose a global optimization algorithm which is stochastic, iterative and population-based like genetic algorithms and particle swarm optimization. In this method, the intra and inter group competitions among parties in a parliament, trying to take the control of the parliament are simulated. Performance of this method for function optimization over some benchmark multi-dimensional functions, of which global and local minimums are known, is compared with traditional genetic algorithms.
Artificial Immune Systems (AIS) - A New Paradigm for Heuristic Decision Making  [PDF]
Uwe Aickelin
Computer Science , 2008,
Abstract: Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a step-by-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here.
Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms  [PDF]
Matthew Crossley,Andy Nisbet,Martyn Amos
Computer Science , 2012, DOI: 10.1007/978-3-642-37213-1_12
Abstract: A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape analysis. Techniques already exist for measuring the "difficulty" of specific landscapes, but these are often designed solely with evolutionary algorithms in mind, and are generally specific to discrete optimisation. In this paper we develop an approach for comparing a wide range of continuous optimisation algorithms. Using a fitness landscape generation technique, we compare six different nature-inspired algorithms and identify which methods perform best on landscapes exhibiting specific features.
Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms  [PDF]
Tomas Philip Runarsson,Juan J. Merelo-Guervos
Computer Science , 2009,
Abstract: The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest a strategy that will allow nature-inspired algorithms to obtain results as good as those based on exhaustive search strategies; in order to do that, we first review, compare and improve current approaches to solving the puzzle; then we test one of these strategies with an estimation of distribution algorithm. Finally, we try to find a strategy that falls short of being exhaustive, and is then amenable for inclusion in nature inspired algorithms (such as evolutionary or particle swarm algorithms). This paper proves that by the incorporation of local entropy into the fitness function of the evolutionary algorithm it becomes a better player than a random one, and gives a rule of thumb on how to incorporate the best heuristic strategies to evolutionary algorithms without incurring in an excessive computational cost.
Providing new meta-heuristic algorithm for optimization problems inspired by humans' behavior to improve their positions  [PDF]
Azar,Adel,Seyedmirzaee, Seyedmoslem
International Journal of Artificial Intelligence & Applications , 2013,
Abstract: Nowadays, meta-heuristic algorithms have earned special position in optimization problems, particularlynonlinear programming. In this study, a new meta-heuristic algorithm called "Improvement of position(IMPRO Algorithm)" is recommended to solve the optimization problems. This algorithm, similar to otherheuristic and meta-heuristic algorithms starts with production of random numbers. However, theaforementioned algorithm is inspired by humans’ behavior to enhance the position which coincidentallydetects the best position with respect to various conditions. Subsequently, a position with the least standarddeviation (0.01) is created surrounding random numbers around the situation in the form of normaldistribution. Afterwards, the new top position is considered and the two positions are compared and the topposition is determined. Thus the conditions which created the best position are situated as the motionfactors. Naturally, the motion direction is toward the opposite direction of the lower position. Next, thisalgorithm changes a response during the search process and solves the problem by utilizing the firmdecisions.
Optimisation of Quantum Evolution Algorithms  [PDF]
Apoorva Patel
Physics , 2015,
Abstract: Given a quantum Hamiltonian and its evolution time, the corresponding unitary evolution operator can be constructed in many different ways, corresponding to different trajectories between the desired end-points. A choice among these trajectories can then be made to obtain the best computational complexity and control over errors. As an explicit example, Grover's quantum search algorithm is described as a Hamiltonian evolution problem. It is shown that the computational complexity has a power-law dependence on error when a straightforward Lie-Trotter discretisation formula is used, and it becomes logarithmic in error when reflection operators are used. The exponential change in error control is striking, and can be used to improve many importance sampling methods. The key concept is to make the evolution steps as large as possible while obeying the constraints of the problem. In particular, we can understand why overrelaxation algorithms are superior to small step size algorithms.
Nature inspired algorithms and artificial intelligence
Elisa Valentina Onet,Ecaterina Vladu
Journal of Computer Science and Control Systems , 2008,
Abstract: Artificial intelligence has been very muchinterested in studying the characteristics ofintelligent agent, mainly planning, learning,reasoning (making decisions) and perception.Biological processes and methods have beeninfluencing science from many decades. Naturalsystems have many properties that inspiredapplications - self-organisation, simplicity of basicelements, dynamics, flexibility. This paper is a surveyof nature inspired algorithms, like Particle SwarmOptimization (PSO), Ant Colony Optimization (ACO)and Artificial Bee Colony(ABC).
Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis
Katerina Tashkova, Peter Koro?ec, Jurij ?ilc, Ljup?o Todorovski, Sa?o D?eroski
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-159
Abstract: We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input.Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.Reconstructing the structure and behavior of biological systems is of fundamental importance to the field of system biology. In general, biological systems exhibit complex nonlinear dynamic behavior, which is often modeled using ordinary differential equations (ODEs). A common approach t
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