Specific crossover and mutation operators for a grouping problem based on interaction data in a regional science context

Enhancing optimal feeder assignment of the multi-head surface mounting machine using genetic algorithms

Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among a variety of crossover operators, there has been a growing interest in multi-parent crossover operators in evolutionary computation. The main motivation of those schemes is establishing comprehensive collective collaboration of more than two chromosomes in the population to generate a new offspring. In this paper, a novel all-parent crossover operator called collective crossover for genetic algorithm is proposed. In this method, all individuals in the current population are involved in recombination part and one offspring is generated. The contribution of each individuals is defined based on its quality in terms of fitness value. The performance of the collective crossover operator is tested on CEC2017 benchmark functions. The results revealed that the proposed crossover operator performs better when compared to well-known two-parent crossover operators including one-point and two-point crossovers. In addition, the differences between collective crossover and the other crossover operators are statistically significant for the most cases.

Cloud computing provides powerful, highly scalable, flexible resources for real world applications. It also reduces the cost and operation expenses. Workflow scheduling is important for getting higher performance, reducing cost and using resources more efficiently in cloud computing. Workflow scheduling in cloud systems assigns tasks to resources available in the system and aims to utilize cloud resources by decreasing makespan of the workflow. In this study, an evolutionary algorithm is proposed to solve workflow scheduling problem. The main objective of this work is to minimize the makespan of the schedule. To achieve this goal, problem specific crossover operator and mutation operators are proposed in the evolutionary algorithm. The crossover operator will combine the problem-specific information stored in both parents to create a new individual. The mutation operators will explore neighbor solutions using some intelligent search mechanisms. This unique design of the operators increases the diversity of the search space and the quality of the solutions. As a result, the workflow schedules obtained from the evolutionary algorithm decreases the makespan of the workflow in the cloud system. The performance of the proposed study is measured using well-known scientific workflows and is compared with the algorithms from the literature. The proposed study outperforms all related algorithms in 67% of the test cases and obtains the same results in the remaining test cases.

Chapter Ten - Differential evolution

The aim of this paper is to show the influence of genetic operators such as crossover and mutation on the performance of a genetic algorithm (GA). The GA is applied to the multi-objective permutation flowshop problem. To achieve our goal an experimental study of a set of crossover and mutation operators is presented. A measure related to the dominance relations of different non-dominated sets, generated by different algorithms, is proposed so as to decide which algorithm is the best. The main conclusion is that there is a crossover operator having the best average performance on a very specific set of instances, and under a very specific criterion. Explaining the reason why a given operator is better than others remains an open problem.

In this paper, a novel approach is proposed to solve the day-ahead multi-objective thermal generation scheduling problem. The proposed method combines the principles of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with problem specific crossover and mutation operators. Heuristics are used in the initial population by seeding the random population with a Priority list based solution for better convergence. The penalty-parameter-less constrained binary tournament method is used as the selection operator to efficiently handle the constraints. Constrain-domination relation is used as the non-dominated classification procedure to classify the population into non-dominated fronts in presence of constraints. Lambda-iteration method is probabilistically used for assigning the economic/environmental real power dispatch to solve the problem. The proposed method is effectively applied to a large scale 60 generating unit power system for short-term generation scheduling problem. It is found that the presented approach gives good convergence to obtain the Pareto-optimal solutions.

This paper estimates endogenous nodal attractions with exogenous nodal spatial interaction and impedance data with a specific unconstrained gravity model. An algebraic simplification method is developed to use minimum inter-nodal interaction data to derive satisfactory nodal attraction estimates. As applied to a 5x5 origin-destination case, the method can potentially be used for large-scale spatial interaction analyses in which obtaining a complete inter-nodal interaction matrix is impossible or a reduction of computation is necessary.

: Current mission preparation and analysis methods place an undue burden of effort on conventional and special operations forces to effectively synchronize and execute their increasingly complex operational responsibilities in a rapidly changing global environment. This thesis developed a tool for the United States Special Operations Command (USSOCOM) in support of their Mission Planning, Analysis, Rehearsal, and Execution (MPARE) initiative to allow special operations forces commanders and staffs to conduct mission planning and analysis in a distributed environment, and rapidly produce dynamic synchronization matrices and scheduling products. Operations research methods provide the foundation for the analysis. The system developed in this thesis is called the Special Operations Mission Planning and Analysis Support System (SOMPASS). SOMPASS is simple to learn and operate, provides dynamic changes with little effort, and is universal in application. This system has the capability to execute on any hardware platform, operate across any network connection, and expand easily to support additional users and requirements. This thesis provides not only a demonstration of capabilities through a special operations oriented illustrative scenario, but also a working product that can be adapted for use in mission planning and analysis by all units under USSOCOM.

Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations.

Definition:Spatial interaction refers to all cases in which a relationship (a flow of persons, goods, communications, citations, and so on) is observed between pairs of spatial units. Differently from the standard areal or point units often analyzed in spatial science, spatial interaction data are denoted by their bilateral nature. They have an origin and a destination (in the particular case, the two being the same). Such origins and destinations may have observable characteristics. Moreover, each pair of spatial units may be described according to some measures of separation (such as distance) or of commonality (e.g., the presence of a common language). Such descriptors are typically employed in spatial interaction models, the most commonly used analytical framework for modeling flow data. Problems occur when the aforementioned descriptors exhibit correlation between the values of different spatial units or dyads which is due to the spatial configuration of the units themselves (e.g., ...This is an excerpt from the content.

A recent focus in the academic literature on segregation indices is on the development of spatial measures, which make use of information on the relative spatial locations of zones; such measures overcome the spatial location independence of traditional indices, often expressed as the ‘checkerboard problem’. Most spatial measures are based on contiguity weighting - immediate neighbours of a given zone are included in calculations - or distance-decay functions. This chapter considers the restrictions of such approaches and discusses some ways in which distance decay weighting schemes can be adapted to better reflect interactions between people in different areas. The use of data on internal migration as a proxy for the daily interactions between Catholics and Protestants in Northern Ireland is considered, and is shown to depict limited interactions between those on opposite sides of so-called ‘interface areas’. Comparison of results using geographically weighted distance-decay schemes suggests that simple distance-based approaches may, in some cases, have limited practical utility if the derived measures are to be used as a guide to the degree of mixing between members of different population groups.

In the era of big data, large amounts of detailed spatial interaction data are readily available due to the increasing pervasiveness of location-aware devices and techniques. Such data are often applied in the research of spatial structures, land use characteristics, and human activity regularities. However, visualizing such data on maps is a great challenge. Existing approaches either encounter overlapping and intersection problems, which may cause information loss or distortion, or are unable to present interaction characteristics. Consequently, the functional and positional features of places and human mobility trends are not fully demonstrated. We propose direction-based pattern maps, a new visualization method to display the spatial interaction pattern of every place by aggregating spatial interaction data in cardinal directions. This approach can well represent local interaction characteristics and is applicable to different spatial scales. Owing to regular hexagonal tessellations, it can avoid cluttering problems and maintain a geographical context. A case study using Beijing taxi trip data is conducted to validate the usefulness of our approach.

This study aims at developing a data‐driven and bottom‐up spatial statistic method for identifying regions of anomalous spatial interactions (clusters of extremely high‐ or low‐value spatial flows), based on which it creates a spatial flow weights matrix. The method, dubbed flowAMOEBA, upgrades a multidirectional optimum ecotope‐based algorithm (AMOEBA) from areal data to spatial flow data through a proper spatial flow neighborhood definition. The method has the potential to dramatically change the way we study spatial interactions. First, it breaks the convention that spatial interaction data are always collected and modeled between spatial entities of the same granularity, as it delineates the OD region of anomalous spatial interactions, regardless of the size, shape, scale, or administrative level. Second, the method creates an empirical spatial flow weights matrix that can handle network autocorrelation embedded in spatial interaction modeling, thus improving related policy‐making or problem‐solving strategies. flowAMOEBA is tested and demonstrated on a synthetic data set as well as a county‐to‐county migration data set.

Spatial statistics: A quantitative geographer’s perspective

This paper addresses the theme of spatial autocorrelation impacting spatial equilibria, and hence an understanding of economic network resilience and vulnerability. It exploits the notion that spatial autocorrelation in the geographic distribution of origin and destination attributes and network autocorrelation in the flows between origins and destinations constitute two spatial autocorrelation components contained in spatial interaction data. It illustrates that a spatial interaction model specification needs to incorporate both components in order to furnish sound implications about associated economic network resilience and vulnerability. Such models also need to undergo sensitivity analyses in terms of changes in geographic scale and resolution. And, it furnishes a novel 3-D visualization of geographic flows, such as journey-to-work trips, in order to achieve a better comprehension of economic network resilience and vulnerability.