| Document Type
|
:
|
BL
|
| Record Number
|
:
|
865415
|
| Main Entry
|
:
|
EvoCOP (Conference)(18th :2018 :, Parma, Italy)
|
| Title & Author
|
:
|
Evolutionary computation in combinatorial optimization : : 18th European Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018, Proceedings /\ Arnaud Liefooghe, Manuel López-Ibáñez (eds.).
|
| Publication Statement
|
:
|
Cham, Switzerland :: Springer,, 2018.
|
| Series Statement
|
:
|
Lecture notes in computer science,; 10782
|
|
|
:
|
LNCS sublibrary. SL 1, Theoretical computer science and general issues
|
| Page. NO
|
:
|
1 online resource (xiv, 189 pages) :: illustrations
|
| ISBN
|
:
|
3319774484
|
|
|
:
|
: 3319774492
|
|
|
:
|
: 9783319774480
|
|
|
:
|
: 9783319774497
|
|
|
:
|
9783319774480
|
| Notes
|
:
|
Includes author index.
|
|
|
:
|
International conference proceedings.
|
| Contents
|
:
|
Intro; Preface; Organization; Contents; Better Runtime Guarantees via Stochastic Domination; 1 Introduction; 2 Stochastic Domination; 3 Domination-Based Fitness Level Method; 4 Beyond the Fitness Level Theorem; 5 Structural Domination; 6 Conclusion; References; On the Fractal Nature of Local Optima Networks; 1 Introduction; 2 Background; 2.1 The Study of Fitness Landscapes; 2.2 The Local Optima Network; 2.3 The Fractal Dimension; 2.4 Fractals and Fitness Landscapes; 2.5 Fractals and Complex Networks; 3 Experimental Setting; 3.1 Test Problem; 3.2 Metaheuristics; 3.3 Fractal Analysis; 4 Results.
|
|
|
:
|
2 Related Work3 Proposed Approach; 3.1 Grammar and the Heuristic Search Space; 3.2 Automatic Design Using irace; 4 Experiments and Results; 4.1 Tuning with a Single Instance Set; 4.2 Tuning with a Random Instance Set; 5 Conclusions; References; Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time; 1 Introduction; 2 Automatic Algorithm Configuration; 2.1 Grammar and Components; 2.2 Solution Representation; 2.3 Search Strategy; 3 Computational Experiments; 3.1 Benchmarks; 3.2 Experimental Setup; 3.3 Results; 4 Conclusions.
|
|
|
:
|
4.1 Fractals and Epistasis4.2 Fractal Dimension and Search Performance; 5 Discussion; 5.1 The Fractal Shape of Local Optima Networks; 5.2 Connections with Search Difficulty; 6 Conclusions and Future Work; References; How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes; 1 Introduction; 2 Definitions and Algorithms; 3 Empirical Methodology; 3.1 Instances; 3.2 Sampling Method; 3.3 Performance and Network Metrics; 4 Results and Analysis; 4.1 Visualisation; 4.2 Performance and Network Metrics Results; 4.3 Impact of Perturbation Strength on Success Rate.
|
|
|
:
|
4.4 Correlation Analysis4.5 Correlation Variance Between Instance Classes; 5 Conclusions; References; Worst Improvement Based Iterated Local Search; 1 Introduction; 2 Definitions; 2.1 Fitness Landscapes and Related Concepts; 2.2 Bit-String Landscapes Instances; 3 Worst Improvement Hill-Climbing; 3.1 Pivoting Rules; 3.2 Additional Experiments; 4 Experimental Analysis; 4.1 Experimental Protocol; 4.2 Results; 4.3 ILS Performance and Landscape Features; 5 Conclusion; References; Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming; 1 Introduction.
|
| Abstract
|
:
|
This book constitutes the refereed proceedings of the 18th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events EuroGP, EvoMUSART and EvoApplications. The 12 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration by means of automatic algorithm configuration and hyper-heuristics. Applications cover conventional academic domains such as NK landscapes, binary quadratic programming, traveling salesman, vehicle routing, or scheduling problems, and also include real-world domains in clustering, commercial districting and winner determination.
|
| Subject
|
:
|
Combinatorial optimization-- Data processing, Congresses.
|
| Subject
|
:
|
Evolutionary computation, Congresses.
|
| Subject
|
:
|
Evolutionary programming (Computer science), Congresses.
|
| Subject
|
:
|
Algorithms data structures.
|
| Subject
|
:
|
Artificial intelligence.
|
| Subject
|
:
|
Combinatorial optimization-- Data processing.
|
| Subject
|
:
|
Computers-- Data Modeling Design.
|
| Subject
|
:
|
Computers-- Data Processing.
|
| Subject
|
:
|
Computers-- Intelligence (AI) Semantics.
|
| Subject
|
:
|
Computers-- Programming-- Algorithms.
|
| Subject
|
:
|
Discrete mathematics.
|
| Subject
|
:
|
Evolutionary computation.
|
| Subject
|
:
|
Evolutionary programming (Computer science)
|
| Subject
|
:
|
Mathematical theory of computation.
|
| Dewey Classification
|
:
|
006.3/823
|
| LC Classification
|
:
|
QA76.618
|
| Added Entry
|
:
|
Liefooghe, Arnaud
|
|
|
:
|
López-Ibáñez, Manuel
|
| Parallel Title
|
:
|
EvoCOP 2018
|