Cover image for Evolutionary Computation in Combinatorial Optimization 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
Title:
Evolutionary Computation in Combinatorial Optimization 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
Author:
Ochoa, Gabriela. editor.
ISBN:
9783319164687
Edition:
1st ed. 2015.
Physical Description:
XII, 235 p. 41 illus. online resource.
Series:
Theoretical Computer Science and General Issues, 9026
Contents:
A Biased Random-Key Genetic Algorithm for the Cloud Resource Management Problem -- A Computational Comparison of Different Algorithms for Very Large p-median Problems -- A New Solution Representation for the Firefighter Problem -- A Variable Neighborhood Search Approach for the Interdependent Lock Scheduling Problem -- A Variable Neighborhood Search for the Generalized Vehicle Routing Problem with Stochastic Demands -- An Iterated Local Search Algorithm for Solving the Orienteering Problem with Time Windows -- Analysis of Solution Quality of a Multi objective Optimization-Based Evolutionary Algorithm for Knapsack Problem -- Evolving Deep Recurrent Neural Networks Using Ant Colony Optimization -- Hyper-heuristic Operator Selection and Acceptance Criteria -- Improving the Performance of the Germinal Center Artificial Immune System Using ε-Dominance: A Multi-objective Knapsack Problem -- Mixing Network Extremal Optimization for Community Structure Detection -- Multi-start Iterated Local Search for the Mixed Fleet Vehicle Routing Problem with Heterogeneous Electric Vehicles -- On the Complexity of Searching the Linear Ordering Problem Neighborhoods -- Runtime Analysis of (1 + 1) Evolutionary Algorithm Controlled with Q-learning Using Greedy Exploration Strategy on ONEMAX+ZEROMAX Problem -- The New Memetic Algorithm HEAD for Graph Coloring: An Easy Way for Managing Diversity -- The Sim-EA Algorithm with Operator Auto adaptation for the Multi objective Firefighter Problem -- True Pareto Fronts for Multi-objective AI Planning Instances -- Upper and Lower Bounds on Unrestricted Black-Box Complexity of JUMPn,l -- Using Local Search to Evaluate Dispatching Rules in Dynamic Job Shop Scheduling.
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