Applications of Evolutionary Computation : 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings / edited by Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, Kehinde Oluwatoyin Babaagba.

Format:

Book

Contributor:

Babaagba, Kehinde Oluwatoyin, editor.

Hidalgo Pérez, José Ignacio, editor.

Laredo, Juan Luis Jiménez, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13224

Language:

English

Subjects (All):

Computer science.

Computer science--Mathematics.

Computer engineering.

Computer networks.

Machine learning.

Theory of Computation.

Mathematics of Computing.

Computer Engineering and Networks.

Machine Learning.

Local Subjects:

Theory of Computation.

Mathematics of Computing.

Computer Engineering and Networks.

Machine Learning.

Physical Description:

1 online resource (759 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

Summary:

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions. .

Contents:

Intro

Preface

Organization

Contents

Applications of Evolutionary Computation

An Enhanced Opposition-Based Evolutionary Feature Selection Approach

1 Introduction

2 Moth Flame Optimization

2.1 Binary Moth Flame Optimization

2.2 Binary Moth Flame Optimization for Feature Selection

3 The Proposed Approach

3.1 Initialization Using Opposition-Based Method

3.2 Retiring Flame

4 Experimental Setup and Results

5 Conclusions

References

A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings

2 Conductance-Based Model Description

3 Defining a Benchmark with Known Types of Ion Channels

4 Methodology and Experimental Setup

5 Experimental Results

6 Conclusions

A Mathematical Description of the Models

B Experimental Setup and Parameter Ranges

Swarm Optimised Few-View Binary Tomography

2 Binary Tomographic Reconstruction

3 Swarm Optimisation

4 Constrained Search in High Dimensions

5 Reconstructions

6 Results

7 Discussion

8 Conclusions

Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization

2 The Metaheuristics Studied

2.1 Basin Hopping

2.2 Differential Evolution

2.3 Particle Swarm Optimization

3 The Benchmarking Environment

4 Experimental Setup

Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models

2 Methodology

2.1 Structured Grammatical Evolution

2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems

3 Experimental Setup

3.1 Study Problems

3.2 Configuration of the Algorithms

4 Results

References.

EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python

2 Related Works

3 Methodology

4 Framework Overview

4.1 Parameters

4.2 Datasets

4.3 Clustering with EvoCluster

4.4 Classification

4.5 Evaluation Measures

4.6 Results Management

5 Experiments and Visualizations

6 Conclusion and Future Works

Evolution of Acoustic Logic Gates in Granular Metamaterials

2 Problem Statement

3 Simulation Setup

3.1 2D Granular Simulator

3.2 Optimization Method

4 Results and Discussion

4.1 Evolution of an Acoustic Band Gap

4.2 Evolving an AND Gate

4.3 Evolving an XOR Gate

5 Conclusion and Future Work

Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry

2 The Problem

3 Proposed Approach

3.1 The Model

3.2 Data

3.3 Adversarial Optimization

3.4 Operator (EA1)

3.5 Public Administration (EA2)

4 Experimental Evaluation

4.1 Stochastic Optimization

4.2 Analysis

4.3 Real World Case

5 Conclusions and Future Work

The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization

2 Background

2.1 Two-Body Problem

2.2 Maneuvers in Space

2.3 Lambert Problem

3 Asteroid Routing Problem

4 Optimization Algorithms

4.1 Sequential Least Squares Programming (SLSQP)

4.2 Greedy Nearest Neighbor Heuristic

4.3 Unbalanced Mallows Model (UMM)

4.4 Combinatorial Efficient Global Optimization (CEGO)

5 Experimental Study

5.1 Experimental Methodology

5.2 Results of the Black-Box Setting

5.3 Results of the Informed Setting

On the Difficulty of Evolving Permutation Codes

2 Preliminaries.

3 Incremental Construction with EA

3.1 Evolving Subsets of Permutations

3.2 Iterative Approach

3.3 Fitness Functions

4.1 Experimental Settings

4.2 Results

Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion

2.1 Differential Evolution

2.2 Moving Average

2.3 Population Diversity

2.4 Opposition-Based Learning

4 Experiments and Results

4.1 Experiments over 30 Dimensions

4.2 Experiments over 50 Dimensions

Search-Based Third-Party Library Migration at the Method-Level

2 Background and Motivation

2.1 Background

2.2 Motivating Example

3 Search-Based API Migration

3.1 Solution Representation

3.2 Calculating the Fitness Function

3.3 Genetic Algorithm Operators and Parameters

4.1 Dataset Used

4.2 Metrics Used

4.3 Results

4.4 Discussion and Limitations

5 Related Work

6 Conclusion

Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction

3 Methods

3.1 Individual Encoding

3.2 Optimization Algorithms

4.1 Benchmark Dataset

4.2 Back-Propagation Neural Networks (BPNNs)

4.3 Computational Setup and Data Preparation

Explainable Landscape Analysis in Automated Algorithm Performance Prediction

2 Related Work

3 Automated Algorithm Performance Prediction

4.1 Data

4.2 Regression Models and Their Hyper-parameters

4.3 Evaluation

5 Results and Discussion

Search Trajectories Networks of Multiobjective Evolutionary Algorithms

3 Preliminaries

3.1 Search Trajectory Networks

3.2 Multiobjective Optimisation Problems

4 STN Extension for the Multiobjective Domain

5 Experiments

5.1 Experimental Parameters

5.2 Metrics

5.3 Reproducibility

7 Conclusion

EvoMCS: Optimising Energy and Throughput of Mission Critical Services

3 EvoMCS: Multi-objective Optimization

3.1 Scenario and Technologies

3.2 Evolutionary Algorithm

3.3 Heuristic for Fitness

3.4 Selection Strategy

3.5 Operators to Generate Descendants

4 Experimentation

4.1 Validation Scenarios

4.2 Configuration Parameters

4.3 Evaluation Metrics

4.4 Profiles Validation - Inputs from EvoMCS

5 Results

5.1 Operators for the EvoMCS in H1(E/T)

5.2 Optimal Configurations

5.3 Optimal Profiles in Scenarios with Dense-Environments

RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation

2.2 L-SHADE

3 RWS-L-SHADE

4 Experimental Results

WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution

2 Grammatical Evolution and Differential Evolution

3 Software Description

3.1 Modular Design

3.2 Parallel Execution

3.3 Persistence Layer

3.4 Implementation Technologies

4 WebGE Most Relevant Features

4.1 GUI for Experiments Management

4.2 Cross-fold Validation

4.3 Detailed Statistics

5 Use Case: Vladislavleva-4

A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment.

1 Introduction

1.1 Goals

1.2 Organisation

2 Background and Related Work

2.1 Vibrational Spectroscopy

2.2 Partial Least Squares Regression

2.3 Spectral Pre-processing

2.4 PLSR for Nutrient Assessment

3.1 Representations for the Two Populations for Co-evolution

3.2 Mapping of the Two Populations for Pairwise Evaluations

3.3 The Evaluation Method

4 Experiment Design

4.1 Datasets

4.2 Parameter Settings

5 Results and Discussions

5.1 Comparisons on the Training and Test Performance

5.2 Analyses on the Pre-processing Selection

5.3 Analyses on Feature Selection Results

6 Conclusions and Future Work

Evolutionary Computation in Edge, Fog, and Cloud Computing

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

3 Job Scheduling Structures

4 Structure Optimisation

4.1 Brute Force Search Algorithm

4.2 Genetic Algorithm

4.3 Simulated Annealing Algorithm

5 Simulation Experiments and Results

5.1 Setup

5.2 Experiment 1: Search Algorithm Comparison

5.3 Experiment 2: Server Processing Power Dispersion Impact

5.4 Experiment 3: Task Size Dispersion Impact

5.5 Experiment 4: Job Complexity Impact

Optimising Communication Overhead in Federated Learning Using NSGA-II

2 Fundamental Concepts

2.1 Federated Learning

2.2 Communication Overhead in Distributed Deep Learning

3.1 The Proposed FL-COP Modelling and Formulation

3.2 The Communication-Overhead Reduction Routine

4 Experimental Study and Analysis

4.1 Problem Benchmarks and Experimental Settings

4.2 Experimental Results and Discussion

5 Conclusions and Perspectives

Evolutionary Machine Learning.

Evolving Data Augmentation Strategies.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Jiménez Laredo, Juan Luis Applications of Evolutionary Computation

ISBN:

3-031-02462-1