A study of order based genetic and evolutionary algorithms in combinatorial optimization problems

In Genetic and Evolutionary Algorithms (GEAs) one is faced with a given number of parameters, whose possible values are coded in a binary alphabet. With Order Based Representations (OBRs) the genetic information is kept by the order of the genes and not by its value. The application of OBRs to the Traveling Salesman Problem (TSP) is a well known technique to the GEA community. In this work one intends to show that this coding scheme can be used as an indirect representation, where the chromosome is the input for the decoder. The behavior of the GEA's operators is compared under benchmarks taken from the Combinatorial Optimization arena.(undefined

A competitive comparison of different types of evolutionary algorithms

This paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in Civil Engineering. The introduced optimization methods are: the integer augmented simulated annealing (IASA), the real-coded augmented simulated annealing (RASA), the differential evolution (DE) in its original fashion developed by R. Storn and K. Price and simplified real-coded differential genetic algorithm (SADE). Each of these methods was developed for some specific optimization problem; namely the Chebychev trial polynomial problem, the so called type 0 function and two engineering problems - the reinforced concrete beam layout and the periodic unit cell problem respectively. Detailed and extensive numerical tests were performed to examine the stability and efficiency of proposed algorithms. The results of our experiments suggest that the performance and robustness of RASA, IASA and SADE methods are comparable, while the DE algorithm performs slightly worse. This fact together with a small number of internal parameters promotes the SADE method as the most robust for practical use.Comment: 25 pages, 8 figures, 5 table

Evolutionary L∞ identification and model reduction for robust control

An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do

A new microscopic nucleon-nucleon interaction derived from relativistic mean field theory

A new microscopic nucleon-nucleon (NN) interaction has been derived for the first time from the popular relativistic mean field theory (RMFT) Lagrangian. The NN interaction so obtained remarkably relate to the inbuilt fundamental parameters of RMFT. Furthermore, by folding it with the RMFT-densities of cluster and daughter nuclei to obtain the optical potential, it's application is also examined to study the exotic cluster radioactive decays, and results obtained found comparable with the successfully used M3Y phenomenological effective NN interactions. The presently derived NN-interaction can also be used to calculate a number of other nuclear observables.Comment: 4 Pages 2 Figure

Unexpected impact of D waves in low-energy neutral pion photoproduction from the proton and the extraction of multipoles

Contributions of $D$ waves to physical observables for neutral pion photoproduction from the proton in the near-threshold region are studied and means to isolate them are proposed. Various approaches to describe the multipoles are employed --a phenomenological one, a unitary one, and heavy baryon chiral perturbation theory. The results of these approaches are compared and found to yield essentially the same answers. $D$ waves are seen to enter together with $S$ waves in a way that any means which attempt to obtain the $E_{0+}$ multipole accurately must rely on knowledge of $D$ waves and that consequently the latter cannot be dismissed in analyses of low-energy pion photoproduction. It is shown that $D$ waves have a significant impact on double-polarization observables that can be measured. This importance of $D$ waves is due to the soft nature of the $S$ wave and is a direct consequence of chiral symmetry and the Nambu--Goldstone nature of the pion. $F$-wave contributions are shown to be negligible in the near-threshold region.Comment: 38 pages, 13 figures, 19 tables. Version to be published in Physical Review

Evidence for existence of many pure ground states in 3d ±J\pm J Spin Glasses

Ground states of 3d EA Ising spin glasses are calculated for sizes up to $14^3$ using a combination of genetic algorithms and cluster-exact approximation . The distribution $P(|q|)$ of overlaps is calculated. For increasing size the width of $P(|q|)$ converges to a nonzero value, indicating that many pure ground states exist for short range Ising spin glasses.Comment: 4 pages, 3 figures, 2 tables, 16 reference

A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA is justified in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics

Multi-objective improvement of software using co-evolution and smart seeding

Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner

Automotive drive by wire controller design by multi-objective techniques

The presence of flexibility in automotive drivelines, coupled with nonlinear elements such as gear lash leads to the presence of an undesirable oscillatory acceleration response to step changes in throttle input. This oscillation is generally low frequency (approximately 2–5 kHz) and can be of sufficient amplitude to cause driver discomfort and subjective disappointment with the driveability of the vehicle. A pole placement controller is developed for a ‘‘drive-by-wire’’ (electronically operated throttle) system, with the objective of reducing or eliminating the oscillatory response. The results of an existing factorial study are used to calculate the required number of poles. Due to the inherent nonlinearities present in the system and the various constraints which must be applied to the controller design, the polynomial values for the pole placement controller are selected by the application of multi- objective optimisation. The controller is shown to achieve excellent performance and robustness to parameter variations and operating conditions

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits