Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity
From MaRDI portal
Publication:606874
DOI10.1007/978-3-642-16544-3zbMath1223.68002OpenAlexW1632066424MaRDI QIDQ606874
Publication date: 18 November 2010
Published in: Natural Computing Series (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-642-16544-3
Analysis of algorithms and problem complexity (68Q25) Searching and sorting (68P10) Nonnumerical algorithms (68W05) Combinatorial optimization (90C27) Research exposition (monographs, survey articles) pertaining to computer science (68-02)
Related Items (76)
A quantum computing based numerical method for solving mixed-integer optimal control problems ⋮ Time complexity analysis of evolutionary algorithms on random satisfiable \(k\)-CNF formulas ⋮ The interplay of population size and mutation probability in the \((1+\lambda )\) EA on OneMax ⋮ A runtime analysis of parallel evolutionary algorithms in dynamic optimization ⋮ Towards a runtime comparison of natural and artificial evolution ⋮ Tail bounds on hitting times of randomized search heuristics using variable drift analysis ⋮ A comparative performance analysis of evolutionary algorithms on \(k\)-median and facility location problems ⋮ Variable solution structure can be helpful in evolutionary optimization ⋮ On the analysis of the (1+1) evolutionary algorithm for the maximum leaf spanning tree problem ⋮ Runtime analysis of non-elitist populations: from classical optimisation to partial information ⋮ Superpolynomial lower bounds for the \((1+1)\) EA on some easy combinatorial problems ⋮ MMAS versus population-based EA on a family of dynamic fitness functions ⋮ Tight bounds on the expected runtime of a standard steady state genetic algorithm ⋮ Does comma selection help to cope with local optima? ⋮ Self-adjusting evolutionary algorithms for multimodal optimization ⋮ Fast mutation in crossover-based algorithms ⋮ A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm ⋮ An improved \((1+1)\) evolutionary algorithm for \(k\)-Median clustering problem with performance guarantee ⋮ Runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem ⋮ Analysis of noisy evolutionary optimization when sampling fails ⋮ Runtime analysis for self-adaptive mutation rates ⋮ Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm ⋮ Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift ⋮ Analysis of the \((1 + 1)\) EA on subclasses of linear functions under uniform and linear constraints ⋮ Single- and multi-objective evolutionary algorithms for the knapsack problem with dynamically changing constraints ⋮ Design and analysis of different alternating variable searches for search-based software testing ⋮ Improved time complexity analysis of the simple genetic algorithm ⋮ Analysis of speedups in parallel evolutionary algorithms and \((1 + \lambda)\) EAs for combinatorial optimization ⋮ Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II) ⋮ Multi-objective evolutionary algorithms are generally good: maximizing monotone submodular functions over sequences ⋮ First Steps Towards a Runtime Analysis of Neuroevolution ⋮ Biobjective optimization problems on matroids with binary costs ⋮ The cost of randomness in evolutionary algorithms: crossover can save random bits ⋮ Hybridizations of evolutionary algorithms with large neighborhood search ⋮ On the approximation ability of evolutionary optimization with application to minimum set cover ⋮ Choosing the right algorithm with hints from complexity theory ⋮ Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter ⋮ An extended jump functions benchmark for the analysis of randomized search heuristics ⋮ Simulated annealing is a polynomial-time approximation scheme for the minimum spanning tree problem ⋮ Runtime analysis for permutation-based evolutionary algorithms ⋮ Black-box search by unbiased variation ⋮ Do additional target points speed up evolutionary algorithms? ⋮ The use of tail inequalities on the probable computational time of randomized search heuristics ⋮ Fixed-parameter evolutionary algorithms and the vertex cover problem ⋮ On the convergence of biogeography-based optimization for binary problems ⋮ The unbiased black-box complexity of partition is polynomial ⋮ Evolutionary algorithms and dynamic programming ⋮ Exponential upper bounds for the runtime of randomized search heuristics ⋮ The \((1+\lambda)\) evolutionary algorithm with self-adjusting mutation rate ⋮ Solving problems with unknown solution length at almost no extra cost ⋮ Running time analysis of the \((1+1)\)-EA for OneMax and LeadingOnes under bit-wise noise ⋮ Sorting by swaps with noisy comparisons ⋮ Reoptimization time analysis of evolutionary algorithms on linear functions under dynamic uniform constraints ⋮ Runtime analysis of evolutionary algorithms via symmetry arguments ⋮ Optimizing linear functions with the \((1 + \lambda)\) evolutionary algorithm -- different asymptotic runtimes for different instances ⋮ Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change ⋮ Runtime analysis of ant colony optimization on dynamic shortest path problems ⋮ An analysis on recombination in multi-objective evolutionary optimization ⋮ Performance analysis of the \((1+1)\) evolutionary algorithm for the multiprocessor scheduling problem ⋮ The runtime of the compact genetic algorithm on jump functions ⋮ Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions ⋮ Optimal mutation rates for the \((1+\lambda)\) EA on OneMax through asymptotically tight drift analysis ⋮ Static and self-adjusting mutation strengths for multi-valued decision variables ⋮ A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem ⋮ On the runtime analysis of the simple genetic algorithm ⋮ The Max problem revisited: the importance of mutation in genetic programming ⋮ Running time analysis of the (1+1)-EA for robust linear optimization ⋮ Putting continuous metaheuristics to work in binary search spaces ⋮ Convergence of set-based multi-objective optimization, indicators and deteriorative cycles ⋮ Cover-encodings of fitness landscapes ⋮ Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem ⋮ Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms ⋮ Trees social relations optimization algorithm: a new swarm-based metaheuristic technique to solve continuous and discrete optimization problems ⋮ On the effectiveness of immune inspired mutation operators in some discrete optimization problems ⋮ An order-based algorithm for minimum dominating set with application in graph mining ⋮ Fitness levels with tail bounds for the analysis of randomized search heuristics
This page was built for publication: Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity