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Found 9 papers, 0 papers with code
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials is Best
On the other hand, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes.
On Inversely Proportional Hypermutations with Mutation Potential
In this paper we prove that considerable speed-ups in the exploitation phase may be achieved with dynamic inversely proportional mutation potentials (IPM) and argue that the potential should decrease inversely to the distance to the optimum rather than to the difference in fitness.
On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms
It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems.
Fast Artificial Immune Systems
Various studies have shown that characteristic Artificial Immune System (AIS) operators such as hypermutations and ageing can be very efficient at escaping local optima of multimodal optimisation problems.
Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem
In this paper we perform an analysis for the standard NP-hard \partition problem from combinatorial optimisation and rigorously show that hypermutations and ageing allow AISs to efficiently escape from local optima where standard EAs require exponential time.
When Hypermutations and Ageing Enable Artificial Immune Systems to Outperform Evolutionary Algorithms
Unless the stop at first constructive mutation (FCM) mechanism is applied, we show that hypermutations require exponential expected runtime to optimise any function with a polynomial number of optima.
Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms
We present a lower bound for a greedy (2+1) GA that matches the upper bound for populations larger than 2, rigorously proving that 2 individuals cannot outperform larger population sizes under greedy selection and greedy crossover up to lower order terms.
Level-based Analysis of Genetic Algorithms and other Search Processes
Finally, we prove that the theorem is nearly optimal for the processes considered.
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
For the generalised minimum spanning tree problem, we analyse the two approaches presented by Hu and Raidl (2012) with respect to the number of clusters that distinguish each other by the chosen representation of possible solutions.
