A survey of evolutionary algorithms using metameric representations

Ryerkerk, Matt; Averill, Ronald C.; Deb, Kalyanmoy; Goodman, Erik D.

doi:10.1007/s10710-019-09356-2

Cited by 42 publications

(24 citation statements)

References 109 publications

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“…2(a) are 2k 1 , 2k 2 , and 2k N P , respectively. As already stated, since the length of each individual in the population is not fixed, the special mutation and crossover operators need to be adopted in this kind of approach [17]. It is clear that the dimension of the search space may change from generation to generation for each individual, thus causing confused search.…”

Section: A Motivationmentioning

confidence: 99%

“…Because of the variable number of stop points in this paper, the length of each individual in the population is not fixed. As pointed out in [17], under this condition, it is necessary to modify the traditional fixedlength mutation and crossover operators in EAs, such as the n-point (most commonly, one-point or two-point) crossover operator 1 in genetic algorithm (GA) and the mutation operator "DE/rand/1" in differential evolution (DE). To this end, two kinds of attempts have been made in previous studies:…”

Section: Introductionmentioning

confidence: 99%

“…• Designing special mutation and crossover operators: One representative is the cut-and-splice operator. It is similar to the commonly used n-point crossover operator, but the locations of crossover points of two individuals do not have to be the same [17]. So far, this operator has been applied in wireless transmitter placement [18], long term evolution network planning [19], the carpool service problem [20], and pixel classification [21].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, two new mutation operators are presented. The former inserts a randomly generated variable behind a random position of an individual, and the latter removes randomly chosen variables from an individual [17]. Note, however, that this kind of approach faces variable-dimension search spaces, which will guide the search in a messy way since the search spaces with different dimensions have different optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

“…However, the introduction of auxiliary variables increases the length of individuals. As a result, this kind of approach may suffer 1 In the n-point crossover operator, the locations of crossover points in two individuals are generally the same [17].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

Huang

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV's deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV's deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori, the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV's deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV's deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness.

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Section: A Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%