A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

The theory of evolutionary computation for discrete search spaces has made significant progress since the early 2010s. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical insights on parameter tuning and parameter control, and summarizes the latest advances for stochastic and dynamic problems. We regard how evolutionary algorithms optimize submodular functions, and we give an overview over the large body of recent results on estimation of distribution algorithms. Finally, we present the state of the art of drift analysis, one of the most powerful analysis technique developed in this field.

PGU-450 network heaters modes modelling with CCGT participation in regulating the electrical load in the heating mode

The features of a mathematical model for optimizing the distribution of heat and electricity at a large thermal power plant with a complex composition of equipment as part of traditional heating units and a heating CCGT are considered. The selection and justification of optimization criteria at different stages of preparation and entry of the station to the electricity and capacity market is given. The disadvantages of the previously proposed optimal distribution algorithms are analyzed in relation to thermal power plants with a complex composition of equipment and with a complex scheme for the supply of electricity and heat. A method and algorithm for solving the problem are proposed based on the equivalence of the CHP equipment and the decomposition of the problem taking into account the schemes of electricity and heat output. The description of mathematical optimization methods is given, taking into account the peculiarities of the CCGT operating modes at reduced loads. The requirements for information support when integrating the developed algorithm into the application software of the automated process control system based on the PTC are given.

Projects Distribution Algorithms for Regional Development

This paper aims to find an efficient method to assign different projects to several regions seeking an equitable distribution of the expected revenue of projects. The solutions to this problem are discussed in this paper. This problem is NP-hard. For this work, the constraint is to suppose that all regions have the same socio-economic proprieties. Given a set of regions and a set of projects. Each project is expected to elaborate a fixed revenue. The goal of this paper is to minimize the summation of the total difference between the total revenues of each region and the minimum total revenue assigned to regions. An appropriate schedule of projects is the schedule that ensures an equitable distribution of the total revenues between regions. In this paper, we give a mathematical formulation of the objective function and propose several algorithms to solve the studied problem. An experimental result is presented to discuss the comparison between all implemented algorithms.

Study of estimation of distribution algorithms applied to neuroevolution

Study of estimation of distribution algorithms applied to neuroevolution

Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.

Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.