Reproducibility in Evolutionary Computation

Experimental studies are prevalent in Evolutionary Computation ( EC ), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery ( ACM ) adopted by ACM Transactions on Evolutionary Learning and Optimization ( TELO ). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.

An Evolutionary Computation Approach to Resource Allocation in Container-based Clouds

<p>A container-based cloud is a new trend in cloud computing that introduces more granular management of cloud resources. Compared with VM-based clouds, container-based clouds can further improve energy efficiency with a finer granularity resource allocation in data centers. The current allocation approaches for VM-based clouds cannot be used in container-based clouds. The first reason is that existing research lacks appropriate models that can represent the interaction of allocation features. Many critical features, such as VM overhead, are also not considered in the current models. The second reason is that current allocation approaches do not perform well to the three frequently encountered allocation scenarios, offline allocation, on-line allocation, and multi-objective allocation. Current approaches for these scenarios are mostly based on greedy heuristics that can be easily stuck at local optimum, or meta-heuristics that consider a simplified one-level allocation problem. Evolutionary Computation (EC) is particularly good at solving combinatorial optimization problems for both off-line and on-line scenarios. The overall goal of this thesis is to propose an EC approach to the three allocation scenarios in order to improve the performance of container-based clouds. Specifically, we aim to optimize energy consumption in all the scenarios. An additional objective, availability of the application, is considered for the multi-objective scenario. First, this thesis investigates two promising representations, vector-based and group-based. We propose two novel vector-based (e.g., Single- chromosome Genetic Algorithm (SGA) and Dual-chromosome GA (DGA)) and a group-based GA approaches for the off-line allocation scenario. Cor- responding genetic operators and decoding processes are also developed and evaluated. Two contributions have been made. Firstly, a novel offline model has been proposed based on current models with additional features. It can be used to evaluate allocation algorithms. Secondly, two types of problem representation, vector-based and group-based, are investigated and three novel approaches are proposed. The three approaches are compared with state-of-the-art approaches. The results show that all solutions produced by these approaches are better than the state-of-the-art approaches and group-based GA is the best approach. Second, this thesis proposes a novel genetic programming hyper-heuristic (GPHH) and a cooperative coevolution (CCGP)-based approach for the on-line allocation scenario. These hyper-heuristic methods can automatically generate allocation rules. For the GPHH-based approach, we develop a novel training procedure to generate reservation-based rules for allocating containers to VM instances. For the CCGP-based approach, we introduce a new terminal set and develop a training procedure to generate allocation rules for two-level allocations. We analyze both human-designed rules and generated rules to provide insights for algorithm designers. Two contributions have been proposed for the on-line problem. First, the on-line model for the on-line allocation scenario is developed. Second, a novel terminal set and training procedures are developed. The automatically generated heuristics perform significantly better than the manually designed heuristics. Third, this thesis proposes a multi-objective approach that generates a set of trade-off solutions for the cloud providers to choose from. Our novel approach, namely Nondominated Sorting-Group GA (NS-GGA), combines the group-based representation and the NSGA-II framework. The experimental results are compared with existing approaches. The results show that our proposed NS-GGA approach outperforms all other approaches. We propose two novelties. The first novelty is the multi-objective model including objectives of energy consumption and availability. The second novelty is the NS-GGA approach that combines the group-based representation with NSGA-II. The allocation solutions found by NS-GGA dominate the solutions found by other existing approaches.</p>

An Evolutionary Computation Approach to Resource Allocation in Container-based Clouds

<p>A container-based cloud is a new trend in cloud computing that introduces more granular management of cloud resources. Compared with VM-based clouds, container-based clouds can further improve energy efficiency with a finer granularity resource allocation in data centers. The current allocation approaches for VM-based clouds cannot be used in container-based clouds. The first reason is that existing research lacks appropriate models that can represent the interaction of allocation features. Many critical features, such as VM overhead, are also not considered in the current models. The second reason is that current allocation approaches do not perform well to the three frequently encountered allocation scenarios, offline allocation, on-line allocation, and multi-objective allocation. Current approaches for these scenarios are mostly based on greedy heuristics that can be easily stuck at local optimum, or meta-heuristics that consider a simplified one-level allocation problem. Evolutionary Computation (EC) is particularly good at solving combinatorial optimization problems for both off-line and on-line scenarios. The overall goal of this thesis is to propose an EC approach to the three allocation scenarios in order to improve the performance of container-based clouds. Specifically, we aim to optimize energy consumption in all the scenarios. An additional objective, availability of the application, is considered for the multi-objective scenario. First, this thesis investigates two promising representations, vector-based and group-based. We propose two novel vector-based (e.g., Single- chromosome Genetic Algorithm (SGA) and Dual-chromosome GA (DGA)) and a group-based GA approaches for the off-line allocation scenario. Cor- responding genetic operators and decoding processes are also developed and evaluated. Two contributions have been made. Firstly, a novel offline model has been proposed based on current models with additional features. It can be used to evaluate allocation algorithms. Secondly, two types of problem representation, vector-based and group-based, are investigated and three novel approaches are proposed. The three approaches are compared with state-of-the-art approaches. The results show that all solutions produced by these approaches are better than the state-of-the-art approaches and group-based GA is the best approach. Second, this thesis proposes a novel genetic programming hyper-heuristic (GPHH) and a cooperative coevolution (CCGP)-based approach for the on-line allocation scenario. These hyper-heuristic methods can automatically generate allocation rules. For the GPHH-based approach, we develop a novel training procedure to generate reservation-based rules for allocating containers to VM instances. For the CCGP-based approach, we introduce a new terminal set and develop a training procedure to generate allocation rules for two-level allocations. We analyze both human-designed rules and generated rules to provide insights for algorithm designers. Two contributions have been proposed for the on-line problem. First, the on-line model for the on-line allocation scenario is developed. Second, a novel terminal set and training procedures are developed. The automatically generated heuristics perform significantly better than the manually designed heuristics. Third, this thesis proposes a multi-objective approach that generates a set of trade-off solutions for the cloud providers to choose from. Our novel approach, namely Nondominated Sorting-Group GA (NS-GGA), combines the group-based representation and the NSGA-II framework. The experimental results are compared with existing approaches. The results show that our proposed NS-GGA approach outperforms all other approaches. We propose two novelties. The first novelty is the multi-objective model including objectives of energy consumption and availability. The second novelty is the NS-GGA approach that combines the group-based representation with NSGA-II. The allocation solutions found by NS-GGA dominate the solutions found by other existing approaches.</p>

Evolutionary Computation for Multifaceted Web Service Composition

<p>Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should observe. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no work so far has focused on handling multiple composition facets simultaneously, producing solutions that: (1) are fully functional (i.e. fully executable, with semantically-matched inputs and outputs), (2) employ a variety of composition constructs (e.g. sequential, parallel, and choice constructs), and (3) are optimised according to non-functional Quality of Service (QoS) measurements. The overall goal of this thesis is to propose hybrid Web service composition approaches that consider elements from all three facets described above when generating solutions. These approaches combine elements of AI planning and of Evolutionary Computation to allow for the creation of compositions that meet all of these requirements. Firstly, this thesis proposes two novel approaches for Web service composition with direct representations. The first one is a tree-based approach where the leaf nodes are the atomic services included in the composition and the inner nodes are the structural constructs that shape the composition workflow. The second one is a graph-based approach where the atomic services are the vertices and the edges connecting them form the composition workflow. The two approaches are compared to determine which is most suitable to the QoS-aware fully automated Web service composition problem. Secondly, this thesis proposes novel sequence-based approaches for Web service composition that use an indirect representation, i.e. they encode solutions as sequences of services. By representing solutions in this way, it is possible to initialise and evolve them without having to enforce their functional correctness. Then, before evaluating the fitness of each solution, a decoding algorithm is used to transform the sequence into the corresponding composition. The decoding algorithm builds the workflow using the ordering in the sequence as closely as possible when selecting the next service to be added, while at the same time generating a functionally correct structure. Thirdly, this thesis treats Web service composition as a multi-objective problem, generating a set of trade-off solutions the user can choose from. More specifically, it proposes multi-objective approaches to fully automated Web service composition, which means that conflicting QoS attributes are independently optimised using a variety of representations that support flexible workflow structures. Additionally, a multi-objective and fully automated memetic approach that uses a local search operator to further improve the quality of solutions is proposed. The following major contributions have been made in this thesis. Firstly, two approaches for Web service composition with direct representations were proposed. When the choice construct is not considered, the graph-based approach produces solutions of higher quality than those of the tree-based approach, but the opposite is true when the choice construct is included. Secondly, indirect representation approaches for Web service composition were proposed. These approaches perform well and can produce solutions with better quality than those found by the graph-based approach. Finally, we propose multi-objective approaches to fully automated service composition, employing different problem representations and a local search operator. The multi-objective approaches using the sequence-based representation were found to produce solutions with better overall quality.</p>

Evolutionary Computation for Multifaceted Web Service Composition

<p>Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should observe. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no work so far has focused on handling multiple composition facets simultaneously, producing solutions that: (1) are fully functional (i.e. fully executable, with semantically-matched inputs and outputs), (2) employ a variety of composition constructs (e.g. sequential, parallel, and choice constructs), and (3) are optimised according to non-functional Quality of Service (QoS) measurements. The overall goal of this thesis is to propose hybrid Web service composition approaches that consider elements from all three facets described above when generating solutions. These approaches combine elements of AI planning and of Evolutionary Computation to allow for the creation of compositions that meet all of these requirements. Firstly, this thesis proposes two novel approaches for Web service composition with direct representations. The first one is a tree-based approach where the leaf nodes are the atomic services included in the composition and the inner nodes are the structural constructs that shape the composition workflow. The second one is a graph-based approach where the atomic services are the vertices and the edges connecting them form the composition workflow. The two approaches are compared to determine which is most suitable to the QoS-aware fully automated Web service composition problem. Secondly, this thesis proposes novel sequence-based approaches for Web service composition that use an indirect representation, i.e. they encode solutions as sequences of services. By representing solutions in this way, it is possible to initialise and evolve them without having to enforce their functional correctness. Then, before evaluating the fitness of each solution, a decoding algorithm is used to transform the sequence into the corresponding composition. The decoding algorithm builds the workflow using the ordering in the sequence as closely as possible when selecting the next service to be added, while at the same time generating a functionally correct structure. Thirdly, this thesis treats Web service composition as a multi-objective problem, generating a set of trade-off solutions the user can choose from. More specifically, it proposes multi-objective approaches to fully automated Web service composition, which means that conflicting QoS attributes are independently optimised using a variety of representations that support flexible workflow structures. Additionally, a multi-objective and fully automated memetic approach that uses a local search operator to further improve the quality of solutions is proposed. The following major contributions have been made in this thesis. Firstly, two approaches for Web service composition with direct representations were proposed. When the choice construct is not considered, the graph-based approach produces solutions of higher quality than those of the tree-based approach, but the opposite is true when the choice construct is included. Secondly, indirect representation approaches for Web service composition were proposed. These approaches perform well and can produce solutions with better quality than those found by the graph-based approach. Finally, we propose multi-objective approaches to fully automated service composition, employing different problem representations and a local search operator. The multi-objective approaches using the sequence-based representation were found to produce solutions with better overall quality.</p>

GECCO 2021

The 2021 Genetic and Evolutionary Computation Conference (GECCO 2021 @ Lille, https://gecco-2021.sigevo.org/) was organized as an online-only conference on July 10-14. After 2020, this is the second time that GECCO is going online. This report describes the virtual environments used for the conference and provides statistics about the GECCO virtualization.