Why evolutionary algorithm

CrossRef Google Scholar. A Davidor, Y. Davis, L. De Jong, K. Dhar, V. Eiben, A. Eshelman, L. Fogel, D. Thesis, University of California, San Diego, Fogel, L. Forrest, S. Glover, F. Goldberg, D. Gorges-Schleuter, M. Grefenstette, J. Hadj-Alouane, A. Holland, J. Homaifar, A. Joines, J. Jones, T. Julstromn, B. Kinnear, K. Koza, J. Le Riche, R. McDonnell, J. Michalewicz, Z. Miihlenbein, H. Nadhamuni, P. L Legacy Core Banking Systems.

T Thick Data. Or try searching another term. Evolutionary Algorithm What is an evolutionary algorithm? What are the business benefits of evolutionary algorithms? Multiple business benefits are associated with evolutionary algorithms, including: Increased flexibility.

Evolutionary algorithm concepts can be modified and adapted to solve the most complex problems humans face and meet target objectives. Better optimization. In order to connect the calculated viewpoints, the authors use an EP algorithm for the traveling salesman problem. It was shown that the computed and planned viewpoints reduce human effort when used as starting points for scene tour.

The proposed method was tested on real terrain and road network datasets. What lesson for a potential user of evolutionary computation emerges from the above overview?

The question is simple, but the answer is hard. All discussed methods are from the same family—evolutionary approaches to optimization problems. The principal question could be: Which of the discussed methods is suitable for the given problem?

Expanding the answer to all heuristic methods in general, not just evolutionary algorithms, the best answer seems to be: take the method you know best, you can define your problem well in terms required by this method, you understand the sensitivity of this method to parameters, you can fine-tune this method. Let us see, for example, on energy fuels area. Numerous evolutionary approaches are applied within this scope. It is not possible to indicate one of them as the best for this particular subject.

The similar situation is with other areas of industry. As we can see, the literature on the evolutionary algorithms in general and in their industrial applications is plentiful, but very rarely this literature concerns applications that have been used in practice. Following [ ], we can say that the theory does not support the practice; there is a big gap between theory and practice.

Theoretical results on properties such as convergence, diversity, exploration, exploitation, deceptiveness, and epistasis are not useful enough for practice. Significant topics from the practice point of view are constraint and noise handling methods, robustness, or multi-objective optimization. The progress in the above matters is also observed; however, these methods are tested mainly on simple silo problems or standard sets of numerical functions, so their usefulness to practitioners working on EA-based software applications is very limited.

It is worth mentioning that the real usefulness of EAs could be not only in industry. The spectacular achievement of EA is presented in [ ]. The artificial intelligence system, with the use of EA, the first time discovered a new theory, namely a mechanism of planar regeneration. The remarkable ability of these small worms to regenerate body parts made them a research model in human regenerative medicine.

As it is shown in this paper, the evolutionary algorithms are a popular research domain. Each year many new modifications of these algorithms are proposed. Some of these modifications are shortly described in Tables 1 and 2. The EAs are applied to solve many industry problems. When we cannot use a dedicated algorithm for a given problem, one of the EAs will be a good choice. Of course, we must remember about specific issues the user can face when dealing with EAs.

Here, we can mention two main problems. First of this problem is a premature convergence the population converging to a suboptimal solution instead of an optimal one. We can solve this problem by introducing the mechanism which will provide a lower transfer rate of the genetic material between individuals—the whole population is divided into several subpopulations so-called islands and periodically migrate an individual between islands [ 15 ].

Another solution of the premature convergence problem is a cooperation of EAs with branch and bound algorithm endowed with interval propagation techniques, as it was shown in [ ].

The second problem is related to the optimal trade-off between exploration and exploitation properties of EA. One of the solutions to this problem is control of the level of selection pressure [ ]. We can do this by introducing specialized genetic operators which will guarantee high population diversity at the start of the algorithm operation high exploration property—small exploitation property and a low population diversity at the end of the algorithm operation low exploration property—high exploitation property.

A survey about exploration and exploitation in EAs can be found in [ ]. As future trends in EAs, we can mention some main directions. The first of current trend is a hybridization of two or more algorithms to obtain better results.

Currently, in the literature, we can find an increasing number of papers where hybrid algorithms are presented. Also, many researchers work on modifications of EAs to improve their computational performance. An interesting domain of future research in EAs is also memetic algorithms. The term memetic algorithm is widely used as a synergy of the evolutionary algorithm or any other population-based approach with separate local search techniques as the Nelder—Mead method.

We can find very interesting information about future trends in EAs in the paper [ ] written by Eiben et al. As one of the future trends in EAs, the authors point out the increasing interest in applying EAs to embodied or embedded systems, that is, employing evolution in populations for which the candidate solutions are controllers or drivers that implement the operational strategy for some situated entities, and are evaluated within the context of some rich, dynamic environment: not for what they are, but for what they do.

Finally, there is another one important issue especially in the industrial application of EA methods. Very often in real-world problems, we must optimize a function in a high-dimensional domain. This process usually is very complex and takes a lot of computational time. Therefore, in real applications, the EAs designed for this type of problems should be designed to be implemented easily to run in parallel or easy to run in GPU to reduce their computational time.

A greater effort in this feature should be in future proposals because this could be a crucial feature to decide whether an algorithm is useful in real applications.

Some research in the area of EAs can be connected with the so-called surrogate models computationally cheaper models of real-world problems which can be used in the place of full fitness evaluation, and that refine those models through occasional full evaluations of individuals in the population [ ].

Also, very often industry problems have many objectives. In tandem with algorithmic advances, the interactive evolutionary algorithms are used to increase the efficiency of EAs in multi-objective optimization [ ]. As we know, each engineering problem is defined by the different objective function and has a different landscape of search space.

Therefore, searching for new techniques in such area as automated tuning and adaptive parameter control is still a hot topic in EAs. Another important issue in the industrial application of EA methods is a proper definition of an objective function.

The industrial problems are very complex. Therefore, a definition of a good mathematical model good objective function for EAs for a given industry process is also a very demanding task. The next issue which we want to mention in discussing is repeatability of the EA methods.

As we know, the EAs are stochastic techniques. Each time the EA method is run, a different result can be obtained. Therefore, the main focus should be on ensuring repeatability of the results generated by EA techniques. This issue is very important for application on EA methods in industry.

In summary, we believe that in the future, new evolutionary algorithms will be developed, and the research problems connected with evolutionary algorithms will always be a hot topic for researchers. Holland JH Adaptation in natural and artificial systems. MIT Press, Cambridge. Google Scholar. Koza J Genetic programming: on the programming of computers by means of natural selection.

Storn R, Price K Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim — Rechenberg I Evolutionstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog Verlag, Stuttgart. Wiley, New York. Slowik A, Kwasnicka H Nature inspired methods and their industry applications—swarm intelligence algorithms.

Rutkowski L Computational intelligence: methods and techniques. Springer, Berlin. Krishnakumar K Micro-genetic algorithms for stationary and non-stationary function optimization. In: SPIE proceedings: intelligent control and adaptive systems, pp — Manderick B, Spiessens P Fine-grained parallel genetic algorithm. In: Proceedings of the third international conference on genetic algorithms, pp — Srinivas N, Deb K Muiltiobjective optimization using nondominated sorting in genetic algorithms.

Evol Comput 2 3 — Rocha LM Contextual genetic algorithms: evolving developmental rules. In: Advances in artificial life, pp — Falkenauer E The grouping genetic algorithm.

In: State of the art in global optimization, pp — Narayanan A, Moore M Quantum-inspired genetic algorithms. Harik GR Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms, Ph.

University of Michigan. J Comput Inf Technol — In: Proceedings of the international conference on parallel problem solving from nature, pp — Takagi H Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89 9 — He Y, Hui C-W Dynamic rule-based genetic algorithm for large-size single-stage batch scheduling.

Comput-Aided Chem Eng — In: Proceedings of the European conference on evolutionary computation in combinatorial optimization, EvoCOP, pp — Deb K, Jain H An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. Ma B, Xia Y A tribe competition-based genetic algorithm for feature selection in pattern classification. Appl Soft Comput — J Comput Des Eng — Expert Syst Appl — In: Proceedings of the 3rd European conference on genetic programming, pp — Ratle A, Sebag M Genetic programming and domain knowledge: beyond the limitations of grammar-guided machine discovery.

Ferreira C Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13 2 — Part I: materials and structural engineering problems. Neural Comput Appl 21 1 — Kattan A, Ong Y Surrogate genetic programming: a semantic aware evolutionary search. Inf Sci — Ffrancon R, Schoenauer M Memetic semantic genetic programming. Swarm Evol Comput — Appl Math Comput — Appl Soft Comput 11 2 — Wang Y, Cai Z, Zhang Q Differential evolution with composite trial vector generation strategies and control parameters.

Yu W, Zhang J Multi-population differential evolution with adaptive parameter control for global optimization. Sci World J , article ID: , 12 pages. Choi TJ, Ahn CW An adaptive cauchy differential evolution algorithm with bias strategy adaptation mechanism for global numerical optimization. J Comput 9 9 — Ali WM RDEL: restart differential evolution algorithm with local search mutation for global numerical optimization.

Egypt Inf J 15 3 — Ghasemi M, Taghizadeh M, Ghavidel S, Abbasian A Colonial competitive differential evolution: an experimental study for optimal economic load dispatch. In: Proceedings of the international workshop on machine learning, optimization, and big data, pp — Swarm and Evol Comput J Syst Eng Electron 27 4 — Slowik A Application of adaptive differential evolution algorithm with multiple trial vectors to artificial neural networks training.

Hansen N, Ostermeier A Completely derandomized self-adaptation in evolution strategies. Evol Comput 9 2 — Soft Comput 17 6 — Evol Comput 2 4 — Arnold DV Weighted multirecombination evolution strategies. Theor Comput Sci 1 — In: Proceedings of the genetic and evolutionary computation conference, pp — Ahrari A, Deb K, Preuss M Multimodal optimization by covariance matrix self-adaptation evolution strategy with repelling subpopulations.

Evol Comput 25 3 — Akimoto Y, Auger A, Hansen N Quality gain analysis of the weighted recombination evolution strategy on general convex quadratic functions. Theor Comput Sci. Iwamatsu M Generalized evolutionary programming with Levy-type mutation. Comput Phys Commun 1—2 — Appl Soft Comput 12 6 — IFAC Proc 47 3 — Gao W Slope stability analysis based on immunised evolutionary programming.

Environ Earth Sci 74 4 — Basu M Fast convergence evolutionary programming for multi-area economic dispatch. Electr Power Compon Syst 45 15 — Evol Algorithms — Intell Artif — Energy Convers Manag — Energy Proc — Przewozniczek MW, Walkowiak K, Aibin M The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks.

Hiassat A, Diabat A, Rahwan I A genetic algorithm approach for local-inventory-routing problem with perishable products. J Manuf Syst — Eur J Oper Res — Energy Build — Reddy S Optimal scheduling of thermal-wind-solar power system with storage. Renew Energy — Bagatur T, Onen F Development of predictive model for flood routing using genetic expression programming. J Flood Risk Manag — Chopra T, Parida M, Kwatra N, Chopra P Development of pavement distress deterioration prediction models for urban road network using genetic programming.

Adv Civ Eng. Article Google Scholar. Yao E, Wang H, Wang L, Xi G, Marechal F Multi-objective optimization and exergoeconomic analysis of a combined cooling, heating and power based compressed air energy storage system.