Communications of the ACM
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
A new approach to estimating the expected first hitting time of evolutionary algorithms
Artificial Intelligence
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
Computing single source shortest paths using single-objective fitness
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Running Time Analysis of ACO Systems for Shortest Path Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Runtime analysis of a binary particle swarm optimizer
Theoretical Computer Science
Ant Colony Optimization and the minimum spanning tree problem
Theoretical Computer Science
Analysis of computational time of simple estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Ant colony optimization and the minimum cut problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant colony optimization for stochastic shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical properties of two ACO approaches for the traveling salesman problem
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
General lower bounds for the running time of evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
General scheme for analyzing running times of parallel evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Fitness-levels for non-elitist populations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Simulated annealing beats metropolis in combinatorial optimization
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Crossover can be constructive when computing unique input–output sequences
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Optimization and Learning
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Analysis of speedups in parallel evolutionary algorithms for combinatorial optimization
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
A large population size can be unhelpful in evolutionary algorithms
Theoretical Computer Science
IEEE Transactions on Evolutionary Computation
An analysis of the behavior of simplified evolutionary algorithms on trap functions
IEEE Transactions on Evolutionary Computation
ACO beats EA on a dynamic pseudo-boolean function
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Fitness levels with tail bounds for the analysis of randomized search heuristics
Information Processing Letters
Hi-index | 5.23 |
For the purpose of analyzing the time cost of evolutionary algorithms (EAs) or other types of randomized search heuristics (RSHs) with certain requirements on the probability of obtaining a target solution, this paper proposes a new index, called the probable computational time (PCT), which complements expected running time analysis. Using simple tail inequalities, such as Markov's inequality and Chebyshev's inequality, we also provide basic properties of PCT, explicitly exhibiting the general relationships between the expected running time and the PCT. To present deeper estimations of the PCT for specific RSHs and problems, we demonstrate a new inequality that is based on the general form of the Chernoff inequality and previous methods such as ''fitness-based partitions'' and ''potential functions'', which have been used to analyze the expected running time of RSHs. The precondition of the new inequality is that the total running time can be described as the sum of a linear combination of some independent geometrically distributed variables and a constant term. The new inequality always provides meaningful upper bounds for the PCT under such circumstances. Some applications of the new inequality for simple EAs, ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms on simple pseudo-Boolean functions are illustrated in this paper.