This is the most popular type of **evolutionary** **algorithm**. Genetic **algorithm** is based upon the process of natural selection and doesn’t require gradient statistics. This **algorithm** is able to find a global error minimum. The genetic **algorithm** with small population size and high mutation rates can find a good solution fastly. This **algorithm** is started with a set of solution called population. Solution from one population are taken and used to form a new population. The new population is better than old population. Solutions which are selected to form new solutions are selected according to their fitness. This process is repeated until some condition is satisfied. In genetic **algorithm**, a population is evolved toward better solution. Each individual solution has a set of properties which can be mutated and altered, solution are represented in binary as string of 0s and 1s. The process of randomly generated population at the starting of **algorithm** is known as iteration or generation. Finally it involved all the steps of **evolutionary** **algorithm** that means selection of individual on the basis of fitness value and then replacement of old individual until the optimum solution is achieved.

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The **evolutionary** **algorithm** is inspired by Darwin’s Theory of Evolution and hence here are many lean words, ex.: individual, chromosome, gene, population, environment, etc. **Evolutionary** **algorithm** is the one, which looks for the best solutions among available ones. Single solution is called individual.

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Abstract: Problem statement: A new multi-objective approach, Strength Pareto **Evolutionary** **Algorithm** (SPEA), is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective **evolutionary** algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering **algorithm** to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.

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The Traveling salesman problem is a famous problem in the field of optimization not only for its practical relevance, but also for the practical application. Many algorithms were devised to solve that problem, but TSP belongs to NP-hard problems, so no **algorithm** has been known to solve this problem in the polynomial time. The most used exact algorithms give exact solution but due to the computational complexity are applied only for solving the relatively small problems. The alternative is the use of **evolutionary** **algorithm**, which can give after finite number of iteration „effective“ solution. This article presents the application of Self Organizing Migrating **Algorithm** and two examples of TSP (8 cities, 25 cities) are given to demonstrate the practical use of SOMA.

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In this paper, GeDEA-II is presented, aiming at reducing the potential weaknesses of its predecessor and competitors, while retaining its very good performance, that is, a good balance between exploration and exploitation. In particular, we propose a different approach to combine the **Evolutionary** **algorithm**-based global search and the Simplex theory, since global exploration and local search are intimately related and performed simultaneously, in such a way that they take advantage from each other. In details, we introduce a novel crossover operator, which we called the “Simplex- crossover” and which will be described hereafter; following this, the individuals created by the proposed **algorithm** via the Simplex-based crossover undergo mutation in a subsequently step, using another new typer of operator, which we called the “Shrink-Mutation”, so as to promote global search capa- bilities of the **algorithm**. Moreover, important modifications have been brought about to the original Simplex theory, in order to enhance further the local search capabilities without penalizing the exploration of the search space.

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In this proposed, we aim to detect the Prankster attack in VANET. To cope with Prankster attack, we put forth a twofold strategy based Genetic **Algorithm**. The genetic **algorithm** will optimize the prankster nodes using fitness function. In the end proposed technique measurement will be done using basic matrices like accuracy, throughput, Bit Error Rate, Packet Delivery Ratio.

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This work is divided as follows. Section 2 introduces the adaptations of an iterative method that uses two phases to solve mathematical programming problems with fuzzy parameters in the objective function and uncertainties in the set of constraints; Section 3 introduces an adaptation of a genetic **algorithm** for the proposed fuzzy problems in this work; Section 4 presents a satisfaction level which tries to establish a tradeoff between α-cut level and the solution of the objective function; Section 5 presents numerical simulations for selected problems and an analysis of the obtained results. Finally, concluding remarks are found in Section 6.

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are abundant in the literature, the application of DM to improve the results of **evolutionary** algorithms is still scarce. The DM module proposed corresponds to an intensiﬁcation strategy, since it tries to discover good features in the best solutions found so far and to apply them in the generation of new solutions. The addition of the DM module into the GA signiﬁcantly improved this method and the hybrid version with local search (GADMLS), on average, produced the better results. Results could be improved if other interactions between modules and/or a more exhaustive set of experiments were conducted (perhaps, larger running times would beneﬁt the more computationally expensive version—GADMLS). Nonetheless, our proposal looks very promising, specially considering problems in which it is difﬁcult to devise efﬁcient local search algorithms.

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find all global solutions, Parsopoulos and Vrahatis [11 ] proposed a modification of the PSO algorithm that relies on a function stretching technique, which is used to e[r]

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To learn a near-optimal Bayesian network structure from a set of target data, efficient optimization **algorithm** is required that searches an exponentially large solution space for near- optimal Bayesian network structure, as this problem was proved to be NP-hard [1]. To find better Bayesian network structures with less time, several efficient search algorithms have been proposed so far. Cooper et al., proposed a well- known deterministic **algorithm** called K2 [2] that searches for near-optimal solutions by applying a constraint of the order of events. As for the general cases without the order constraint, although several approaches have been proposed so far, many of which uses genetic algorithms (GAs), which find good Bayesian network structures within a reasonable time

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In this paper, a DCA optimization **algorithm** based on GA is presented to solve the channel assignment problem. Channel optimization based on GA is suitable because it is robust in optimizing a complex problem and its inherent features enable the **algorithm** to search for a global minimum without being trapped into a local minimum. In this **algorithm**, reasonable convergence speed can be achieved by adapting the population size according to the number of eligible channels for a particular cell upon new call request, instead of maintaining a fixed population size throughout the simulation.

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Unlike mono-objective problems, solving multi-objective prob- lems consists in finding a set of solutions. Indeed, the quality of such solutions is evaluated by metrics which reveal some key fac- tors: set size, closeness of the solution set to the Pareto frontier, and distribution pattern over the n-dimensional space. In this work, the performance of the solutions is evidenced by the hyper- volume (HV) and Inverse Generational Distance (IGD) metrics. HV calculates the volume of the region, in the objective space, which is covered by the non-dominated solutions. Table 5 presents the HV values obtained by IMOEA-DFR. Regarding HV metric, the algo- rithm obtained better results for cases I and III. Table 6 presents the mean values of IGD metric for each run of those three cases. This metric was taken in relation to the estimated Pareto frontier. Smaller IGDs correspond to better convergence of the **evolutionary** **algorithm**. As HV, the IGD metric indicated that IMOEA-DFR

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Manual population inspection as the driving force of a directed selection process (cycle 3) was embedded into the flow of the **evolutionary** **algorithm** (Figure 2) thereby introducing research experience into each optimization cycle. This knowledge was utilized to choose the number of succeeding peptides out of a population that shall - according to their fitness - serve as ‘‘parents’’ for the next generation. The influence of larger and smaller populations of such parent peptide sequences on the optimization process was investigated using sequence data from the second generation (gen2). We compared a choice of the 20 fittest (‘‘very fit’’) versus the 32 fittest peptides (‘‘very fit+fit’’) from gen2 as parent sequences to generate two populations of the third generation (gen3). While we observed that the increase in average fitness of the 22 top candidates of the filial generation was higher with only 20 ‘‘very fit’’ parents (1.8 fold) than with 32 ‘‘very fit+fit’’ parents (1.5 fold), the total number of peptides with high fitness values was lower in the filial population derived from the smaller parent population than in the filial population derived from the larger parent population. We therefore chose a compromise between high increase in average fitness and high number of very fit filial peptides and decided on using the 25 fittest peptides of each generation as parents for the next evolution round from generation 4 onwards.

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Nature inspired some **evolutionary** optimisation algorithms suitable for global optimisation of even non-linear, high-dimensional, multimodal, and discontinuous problems. The original genetic **algorithm** (GA) was developed by Holland (Holland, 1992) and was based on the process of evolution of biological organisms. Recently, approaches like genetic programming and bacterial **evolutionary** **algorithm** present an alternative to the former algorithms. GP optimisation uses the same operators as GA, though it requires an expression tree for gene representation as a combination of functions. The BEA is a simpler **algorithm**, and its operations were inspired by the microbial evolution phenomenon. The current paper focuses on a comparison between the applicability of GP for BNN design and BEA for the optimisation of the fuzzy rule base.

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The parameter values of an **evolutionary** **algorithm** can greatly influence whether the **algorithm** will find a near-optimum solution, and whether it will find such a solution efficiently. Choosing correctly the parameters, however, is a time-consuming task and considerable effort has gone into developing good heuristics for it [MS07]. For instance, Lobo et al. [LLM07] compiled a series of papers presented in the 2005 Genetic and **Evolutionary** Computation Conference, as well as other papers from invited authors in the theme parameter setting in **evolutionary** algorithms, presenting the state of the art in parameter tuning and control. While parameter setting is an important step of this work, a deeper discussion on different strategies for choosing the optimal set of parameters is out of the scope of this dissertation. Table 6.1 presents the default parameter values of E-Motion, and we discuss our choices below.

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Abstract—Evolvable hardware has been implemented in the design of combinational logic circuits. But it always takes too much time if circuits are generated with **evolutionary** **algorithm** directly. S o the repair techniques have been more and more important in evolvable hardware. In this paper, Quine-McCluskey repair technique (QMRT), which generates the repair circuits with Quine-McCluskey (Q-M) **algorithm**, is proposed for **evolutionary** design of combinational logic circuits. Based on the QMRT, a novel **evolutionary** design **algorithm**, named EA-QMRT, is presented. The experimental results demonstrate that the EA-QMRT could realize combinational logic circuit with acceptable time cost and gates needed.

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The results observed in this study are dependent on the setup, composed of a linear probe with limited bandwidth, and the sample used. Different setups could lead to different sensitivity and accuracy in temperature mapping. It is worth noting that different calibration factor values have been reported, depending on the type of tissue analyzed and setup used (Ke et al., 2014; Larina et al., 2005; Shah et al., 2008; Wang et al., 2011). Therefore, the merit of the present paper was to demonstrate that signal parameters and methods involved in tracking PA signal amplitude changes, due to temperature variation, drastically affected the sensitivity and accuracy of thermal images formation. We also demonstrated that a simple **evolutionary** **algorithm** could improve the accuracy of temperature mapping by, on average, 7.5%. Consequently, we strongly advise the researchers interested in using PA imaging to monitor the temperature in hyperthermia procedures to evaluate the sensitivity and accuracy of their method using a similar experiment described here. Optimizing the parameters to improve accuracy is also recommended.

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Surveys of existing methods for multi-objective problems were presented in Jozefowiez et al. (2008) and Zhou et al. (2011). In Jozefowiez et al. (2008), the authors examined multiobjective versions of several variants of the Vehicle Routing Problem (VRP) in terms of their objectives, their characteristics and the types of proposed algorithms to solve them. A survey of the state of the art of the multi-objective **evolutionary** algorithms was proposed by Zhou et al. (2011). This papers covers algorithms frameworks for multiobjective combinatorial problems during the last eight years. However, in the literature reviewed, there are few works considering the multi-objective version of the MDVRPB. Multiobjective metaheuristic approaches for combinatorial problems were presented in Doerner et al. (2004), Liu et al. (2006) and Lau et al. (2009). A multiobjective methodology by Pareto Ant Colony Optimization for solving a portfolio problem was introduced by Doerner et al. (2004). A multi-objective mixed zero-one integer-programming model for the vehicle routing problem with balanced workload and delivery time was introduced by Liu et al. (2006). In this work, a heuristic-based solution method was developed. A fuzzy multi-objective **evolutionary** **algorithm** for the problem of optimization of vehicle routing problems with multiple depots, multiple customers, and multiple products was proposed by Lau et al. (2009). In this work, two objectives were considered: minimization of the traveling distance and also the traveling time.

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Despite these issues, the chapters by David Buss and Joshua Duntley on intimate partner violence, Kevin Beaver et al. on the **evolutionary** behavioral genetics of violent crime, Catherine Salmon and Jessica Hehman on sibling conflict and siblicide, David Bjorklund and Patricia Hawley on the **evolutionary** approach to the development of aggressive behavior, and Cross and Campbell on violence and aggression in Women, all include clear summaries of recent research on their specific topics. Navarrete and McDonald provide an interesting look at the intersection of the study of sexual selection and the study of outgroup psychology by examining gender differences related to intergroup conflict. Joseph Carroll provides a summary of some of the **evolutionary** approaches to violence in literature, and provides several specific examples of his personal approach to **evolutionary** literary criticism. David Herring provides a detailed example of how **evolutionary** theory might be applied to reduce specific forms of violence in his discussion of child welfare law. Robin Vallacher and Christopher Brooks argue creatively that dynamical theory may be a necessary complement to **evolutionary** theory. Catherine Cross and Ann Campbell end the volume with a chapter discussing **evolutionary** explanations of violence and aggression in women. The other two chapters, one by Lawrence Keeley and the other by Steven Leblanc, are the ones most directly linked to Pinker’s The Better Angels of Our Nature because both concern the archaeological and anthropological evidence of warfare in the past. In this sense, these chapters provide what first appears to be a solid foundation for the rest of the book. Unfortunately, there are some cracks in this foundation too large to be ignored.

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Levy correctly asserts that “evolution is the result of a process that systematically favours selfishness” (p. 44). However, **evolutionary** theorists define selfishness in significantly different ways from people who make moral attributions. **Evolutionary** theorists define selfish behaviours as behaviours that enhance an organism’s fitness, or enable it to propagate its genes. Call this type of selfishness “genetic selfishness.” Ordinary people (and philosophers) define selfishness as pursuing one’s own interests (especially profit and pleasure) excessively, at the expense of others. Call this type of selfishness psychological selfishness. When people assume that unselfishness is necessary for morality, they are referring to psychological unselfishness. There is no necessary connection between psychological and genetic (or biological) forms of selfishness; psychologically selfish behaviours that people consider immoral, such as gluttony, greed, cheating, and rape, need not be genetically selfish. Those who seek to maximize their profit and pleasure at the expense of others may well fail to propagate their genes. On the other side of this coin, those who are willing to sacrifice their interests for the sake of others (such as their offspring) may well propagate more of their genes than those who are not. It follows that even if all organisms were genetically selfish (which is not necessarily the case, especially in current environments), this would not render them psychologically selfish or immoral.

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