Characterization of the Performance of Memetic Algorithms for the Automation of Bone Tracking With Fluoroscopy

Tersi, Luca; Fantozzi, Silvia; Stagni, Rita

doi:10.1109/tevc.2013.2281540

Cited by 8 publications

(6 citation statements)

References 53 publications

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“…The selected form of learning in a hybrid genetic algorithm has been shown to affect its performance [37]. Several researchers have compared the performance of pure genetic algorithms with alternative learning strategies in hybrid genetic algorithms [18].…”

Section: Hybrid Lamarckian-baldwinian Modelsmentioning

confidence: 99%

Self-adaptive learning for hybrid genetic algorithms

2020

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Local search can be introduced into genetic algorithms to create a hybrid, but any improvement in performance is dependent on the learning mechanism. In the Lamarckian model, a candidate solution is replaced by a fitter neighbour if one is found by local search. In the Baldwinian model, the original solution is retained but with an upgraded fitness if a fitter solution is found in the local search space. The effectiveness of using either model or a variable proportion of the two within a hybrid genetic algorithm is affected by the topology of the fitness function and the details of the hybrid algorithm. This paper investigates an intelligent adaptive approach to decide on the learning mechanism to be used by an individual over the course of the search. Evolution is used to self-adapt both the frequency of a steepest-descent local search and the relative proportions of Lamarckian and Baldwinian inheritance. Experiments have shown that this form of adaptive learning can improve the ability to find high-quality solutions and can accelerate the hybrid search without the need to find optimal control parameters for the learning process.

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Section: Hybrid Lamarckian-baldwinian Modelsmentioning

confidence: 99%

Self-adaptive learning for hybrid genetic algorithms

2020

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“…In order to incorporate bias in the search for Object 2 , we propose a problem-specific local search operator, and it can be regarded as a kind of memes in the case of MAs (Rubio-Largo et al 2016;Tersi et al 2015;Yuan and Xu 2015). In combinatorial optimization, local search operators generally work in the form of heuristics that are customized to a specific problem.…”

Section: Moma-based Task Hardeningmentioning

confidence: 99%

Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores

Yuan

Chen

et al. 2019

Soft Comput

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The rate of transient faults has increased significantly as the technology scales up. The tolerance of transient faults has become an important issue in the system design. Dual modular redundancy (DMR) and triple modular redundancy (TMR) are two commonly used techniques that can achieve fault detection and masking through executing redundant tasks. As DMR and TMR have different time and cost overheads, we must carefully determine which one should be used for each task (i.e., task hardening) to achieve the optimal system design. Furthermore, for multi-core systems, the system-level design includes the allocation of cores for the tasks (i.e., task mapping) as well. This paper aims at task hardening and mapping simultaneously for independent tasks on multi-cores with heterogeneous performances, in order to minimize the maximum completion time of all tasks (i.e., makespan). We demonstrate that once task hardening is given, task mapping of independent tasks can be achieved by employing min-max-weight perfect matching with a polynomial time complexity. Besides, as there is a trade-off between cost and time performance, we propose a multi-objective memetic algorithm (MOMA)-based task hardening method to obtain a set of solutions with different numbers of cores (i.e., costs), so the designer can choose different solutions according to different requirements. The key idea of the MOMA is to incorporate problem-specific knowledge into the global search of evolutionary algorithms. Our experimental studies have demonstrated the effectiveness of the proposed method and have shown that by combining the results of MOMA and MOEA we can provide a designer with a highly accurate set of solutions within a reasonable amount of time. Keywords Fault tolerance Á Multi-cores Á Task hardening Á Task mapping Á Multi-objective optimization Á Memetic algorithms Communicated by V. Loia.

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“…Thus, some of the constraint-handling methods in the literature are: (i) penalties imposed on infeasible solutions to reduce their fitness; (ii) constraint dominance tournaments that, essentially, impede the survival and propagation of infeasible solutions (Deb et al, 2002); (iii) replacement of infeasible solutions with new solutions created randomly (Tersi et al, 2015); (iv) repair of infeasible solutions (Chootinan and Chen, 2006); and (v) stochastic ranking that involves binary tournaments and probabilistic penalty functions (Runarsson and Yao, 2000). While constraint dominance tournaments may have a strong practical appeal based on the ease of implementation, they tend to reduce diversity in the population of candidate solutions too quickly (Liu et al, 2010;Eskandar et al, 2012;Sheikholeslami and Talatahari, 2016), which, in turn, impedes the progress of the optimization through lack of diversity in the gene pool.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of water distribution networks: real-world case study with penalty-free multi-objective genetic algorithm using pressure-driven simulation

Tanyimboh

Seyoum

2020

WSA

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Water distribution systems are an integral part of the economic infrastructure of modern-day societies. However, previous research on the design optimization of water distribution systems generally involved few decision variables and consequently small solution spaces; piecemeal-solution methods based on pre-processing and search space reduction; and/or combinations of techniques working in concert. The present investigation was motivated by the desire to address the above-mentioned issues including those associated with the lack of high-performance computing (HPC) expertise and limited access in developing countries. More specifically, the article’s aims are, firstly, to solve a practical water distribution network design optimization problem and, secondly, to develop and demonstrate a generic multi-objective genetic algorithm capable of achieving optimal and near-optimal solutions on complex real-world design optimization problems reliably and quickly. A multi-objective genetic algorithm was developed that applies sustained and extensive exploration of the active constraint boundaries. The computational efficiency was demonstrated by the small fraction of 10-245 function evaluations relative to the size of the solution space. Highly competitive solutions were achieved consistently, including a new best solution. The water utility’s detailed distribution network model in EPANET 2 was used for the hydraulic simulations. Therefore, with some additional improvements, the optimization algorithm developed could assist practitioners in day-to-day planning and design.

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Characterization of the Performance of Memetic Algorithms for the Automation of Bone Tracking With Fluoroscopy

Cited by 8 publications

References 53 publications

Self-adaptive learning for hybrid genetic algorithms

Self-adaptive learning for hybrid genetic algorithms

Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores

Design optimization of water distribution networks: real-world case study with penalty-free multi-objective genetic algorithm using pressure-driven simulation

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