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Fast Contiguous Somatic Hypermutations for Single-Objective Optimisation and Multi-Objective Optimisation Via Decomposition Open
Somatic Contiguous Hypermutations (CHM) are a popular variation operator used in artificial immune systems for optimisation tasks. Theoretical studies have shown that CHM operators can lead to considerable speed-ups in the expected optimis…
On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block Mutations Open
In this article, we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the GSGP Fixed Block Mutat…
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best Open
We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax , we rigorously prove that using uniform parent sele…
On Steady-State Evolutionary Algorithms and Selective Pressure: Why\n Inverse Rank-Based Allocation of Reproductive Trials is Best Open
We analyse the impact of the selective pressure for the global optimisation\ncapabilities of steady-state EAs. For the standard bimodal benchmark function\n\\twomax we rigorously prove that using uniform parent selection leads to\nexponent…
Fast Immune System Inspired Hypermutation Operators for Combinatorial Optimisation Open
Various studies have shown that immune system inspired hypermutation operators can allow artificial immune systems (AIS) to be very efficient at escaping local optima of multimodal optimisation problems. However, this efficiency comes at t…
On inversely proportional hypermutations with mutation potential Open
Artificial Immune Systems (AIS) employing hypermutations with linear static mutation potential have recently been shown to be very effective at escaping local optima of combinatorial optimisation problems at the expense of being slower dur…
On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms Open
It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In th…
On the Benefits of Populations on the Exploitation Speed of Standard\n Steady-State Genetic Algorithms Open
It is generally accepted that populations are useful for the global\nexploration of multi-modal optimisation problems. Indeed, several theoretical\nresults are available showing such advantages over single-trajectory search\nheuristics. In…
Fast Artificial Immune Systems Open
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. However, this efficiency comes at…
Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem Open
Typical artificial immune system (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which evolutionary algorithms (EAs) struggle to escape. Such behaviour has been show…
When Hypermutations and Ageing Enable Artificial Immune Systems to\n Outperform Evolutionary Algorithms Open
We present a time complexity analysis of the Opt-IA artificial immune system\n(AIS). We first highlight the power and limitations of its distinguishing\noperators (i.e., hypermutations with mutation potential and ageing) by\nanalysing them…
Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms Open
Explaining to what extent the real power of genetic algorithms (GAs) lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in evolutionary computation. In this paper we show how the…
Level-Based Analysis of Genetic Algorithms and Other Search Processes Open
Understanding how the time complexity of evolutionary algorithms (EAs) depend on their parameter settings and characteristics of fitness landscapes is a fundamental problem in evolutionary computation. Most rigorous results were derived us…
Standard Steady State Genetic Algorithms Can Hillclimb Faster than\n Mutation-only Evolutionary Algorithms Open
Explaining to what extent the real power of genetic algorithms lies in the\nability of crossover to recombine individuals into higher quality solutions is\nan important problem in evolutionary computation. In this paper we show how the\nin…
Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms Open
Explaining to what extent the real power of genetic algorithms lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in evolutionary computation. In this paper we show how the inter…
On the runtime analysis of the opt-IA artificial immune system Open
We present a time complexity analysis of the Opt-IA artificial immune system (AIS). We first highlight the power and limitations of its distinguishing operators (i.e., hypermutations with mutation potential and ageing) by analysing them in…
Level-Based Analysis of Genetic Algorithms and Other Search Processes Open
Understanding how the time-complexity of evolutionary algorithms (EAs) depend on their parameter settings and characteristics of fitness landscapes is a fundamental problem in evolutionary computation. Most rigorous results were derived us…