Memetic simulated annealing for data approximation with local-support curves

This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grants TEC2013-47141-C4-R (RACHEL) and #TIN2012-30768 (Computer Science National Program) and Toho University (Funabashi, Japan)

Prescriptive formalism for constructing domain-specific evolutionary algorithms

It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m..

Metaheuristics for the unit commitment problem : The Constraint Oriented Neighbourhoods search strategy

Tese de mestrado. Faculdade de Engenharia. Universidade do Porto. 199

The falling tide algorithm: A new multi-objective approach for complex workforce scheduling

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Quantum annealing and advanced optimization strategies of closed and open quantum systems

Adiabatic quantum computation and quantum annealing are powerful methods designed to solve optimization problems more efficiently than classical computers. The idea is to encode the solution to the optimization problem into the ground state of an Ising Hamiltonian, which can be hard to diagonalize exactly and can involve long-range and multiple-body interactions. The adiabatic theorem of quantum mechanics is exploited to drive a quantum system towards the target ground state. More precisely, the evolution starts from the ground state of a transverse field Hamiltonian, providing the quantum fluctuations needed for quantum tunneling between trial solution states. The Hamiltonian is slowly changed to target the Ising Hamiltonian of interest. If this evolution is infinitely slow, the system is guaranteed to stay in its ground state. Hence, at the end of the dynamics, the state can be measured, yielding the solution to the problem. In real devices, such as in the D-Wave quantum annealers, the evolution lasts a finite amount of time, which gives rise to Landau-Zener diabatic transitions, and occurs in the presence of an environment, inducing thermal excitations outside the ground state. Both these limitations have to be carefully addressed in order to understand the true potential of these devices. The present thesis aims to find strategies to overcome these limitations. In the first part of this work, we address the effects of dissipation. We show that a low-temperature Markovian environment can improve quantum annealing, compared with the closed-system case, supporting other previous results known in the literature as thermally-assisted quantum annealing. In the second part, we combine dissipation with advanced annealing schedules, featuring pauses and iterated or adiabatic reverse annealing, which, in combination with low-temperature environments, can favor relaxation to the ground state and improve quantum annealing compared to the standard algorithm. In general, however, dissipation is detrimental for quantum annealing especially when the annealing time is longer than the typical thermal relaxation and decoherence time scales. For this reason, it is essential to devise shortcuts to adiabaticity so as to reach the adiabatic limit for relatively short times in order to decrease the impact of thermal noise on the performances of QA. To this end, in the last part of this thesis we study the counterdiabatic driving approach to QA. In counterdiabatic driving, a new term is added to the Hamiltonian to suppress Landau-Zener transitions and achieve adiabaticity for any finite sweep rate. Although the counterdiabatic potential is nonlocal and hardly implementable on quantum devices, we can obtain approximate potentials that dramatically enhance the success probability of short-time quantum annealing following a variational formulation

A Hybrid Genetic Algorithm for the Student-Aware University Course Timetabling Problem

Traditionally, academic institutions schedule courses using constraints that ensure that instructors and courses do not overlap in available rooms and time periods; students\u27 planning needs are rarely taken into account. This problem becomes particularly acute for students in liberal arts institutions, because they have multiple graduation requirements in addition to their chosen academic program. My research builds on the University Course Timetabling Problem (UCTP) to include students\u27 scheduling needs. This approach to the UCTP problem uses a combination of a genetic algorithm and case-based reasoning. To improve the performance of the genetic algorithm, I use a group-based genetic algorithm to place courses into distinct rooms and a self-fertilization crossover operator to avoid adding duplicate courses to the timetable during crossover. Case-based reasoning serves as a system to store and retrieve previous solutions. If a new problem is given, instead of using a genetic algorithm to produce timetables from scratch, the system first checks if the case-base has a previous timetable that solves the problem. I generate test data using knowl- edge of class scheduling at Macalester College. Although the student constraint is harder to satisfy than the instructor constraint, my results show that the genetic algorithm improves the fitness of the population for each generation, and it returns a feasible solution, even for the most constrained benchmarks

Firefly algorithm approach for rational bézier border reconstruction of skin lesions from macroscopic medical images

Image segmentation is a fundamental step for image processing of medical images. One of the most important tasks in this step is border reconstruction, which consists of constructing a border curve separating the organ or tissue of interest from the image background. This problem can be formulated as an optimization problem, where the border curve is computed through data fitting procedures from a collection of data points assumed to lie on the boundary of the object under analysis. However, standard mathematical optimization techniques do not provide satisfactory solutions to this problem. Some recent papers have applied evolutionary computation techniques to tackle this issue. Such works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bézier curves by applying the firefly algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on medical images of melanomas show that this method performs well and can be successfully applied to this problem