9,757 research outputs found
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Memetic algorithms are popular hybrid search heuristics that integrate
local search into the search process of an evolutionary algorithm
in order to combine the advantages of rapid exploitation and global
optimisation. However, these algorithms are not well understood and
the field is lacking a solid theoretical foundation that explains when
and why memetic algorithms are effective.
We provide a rigorous runtime analysis of a simple memetic algorithm,
the (1+1) MA, on the Hurdle problem class, a landscape class
of tuneable difficulty that shows a âbig valley structureâ, a characteristic
feature of many hard problems from combinatorial optimisation.
The only parameter of this class is the hurdle width w, which describes
the length of fitness valleys that have to be overcome. We show
that the (1+1) EA requires Î(n
w) expected function evaluations to
find the optimum, whereas the (1+1) MA with best-improvement and
first-improvement local search can find the optimum in Î(n
2 +n
3/w2
)
and Î(n
3/w2
) function evaluations, respectively. Surprisingly, while
increasing the hurdle width makes the problem harder for evolutionary
algorithms, the problem becomes easier for memetic algorithms.
We discuss how these findings can explain and illustrate the success of
memetic algorithms for problems with big valley structures
Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
A significant challenge in nature-inspired algorithmics is the identification
of specific characteristics of problems that make them harder (or easier) to
solve using specific methods. The hope is that, by identifying these
characteristics, we may more easily predict which algorithms are best-suited to
problems sharing certain features. Here, we approach this problem using fitness
landscape analysis. Techniques already exist for measuring the "difficulty" of
specific landscapes, but these are often designed solely with evolutionary
algorithms in mind, and are generally specific to discrete optimisation. In
this paper we develop an approach for comparing a wide range of continuous
optimisation algorithms. Using a fitness landscape generation technique, we
compare six different nature-inspired algorithms and identify which methods
perform best on landscapes exhibiting specific features.Comment: 10 pages, 1 figure, submitted to the 11th International Conference on
Adaptive and Natural Computing Algorithm
Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation
Many real world problems can be expressed as optimisation problems. Solving
this kind of problems means to find, among all possible solutions, the one that
maximises an evaluation function. One approach to solve this kind of problem is
to use an informed search strategy. The principle of this kind of strategy is
to use problem-specific knowledge beyond the definition of the problem itself
to find solutions more efficiently than with an uninformed strategy. This kind
of strategy demands to define problem-specific knowledge (heuristics). The
efficiency and the effectiveness of systems based on it directly depend on the
used knowledge quality. Unfortunately, acquiring and maintaining such knowledge
can be fastidious. The objective of the work presented in this paper is to
propose an automatic knowledge revision approach for systems based on an
informed tree search strategy. Our approach consists in analysing the system
execution logs and revising knowledge based on these logs by modelling the
revision problem as a knowledge space exploration problem. We present an
experiment we carried out in an application domain where informed search
strategies are often used: cartographic generalisation.Comment: Knowledge Revision; Problem Solving; Informed Tree Search Strategy;
Cartographic Generalisation., Paris : France (2008
Exploring the impact of different cost heuristics in the allocation of safety integrity levels
Contemporary safety standards prescribe processes in which system safety requirements, captured early and expressed in the form of Safety Integrity Levels (SILs), are iteratively allocated to architectural elements. Different SILs reflect different requirements stringencies and consequently different development costs. Therefore, the allocation of safety requirements is not a simple problem of applying an allocation "algebra" as treated by most standards; it is a complex optimisation problem, one of finding a strategy that minimises cost whilst meeting safety requirements. One difficulty is the lack of a commonly agreed heuristic for how costs increase between SILs. In this paper, we define this important problem; then we take the example of an automotive system and using an automated approach show that different cost heuristics lead to different optimal SIL allocations. Without automation it would have been impossible to explore the vast space of allocations and to discuss the subtleties involved in this problem
Darwinian Data Structure Selection
Data structure selection and tuning is laborious but can vastly improve an
application's performance and memory footprint. Some data structures share a
common interface and enjoy multiple implementations. We call them Darwinian
Data Structures (DDS), since we can subject their implementations to survival
of the fittest. We introduce ARTEMIS a multi-objective, cloud-based
search-based optimisation framework that automatically finds optimal, tuned DDS
modulo a test suite, then changes an application to use that DDS. ARTEMIS
achieves substantial performance improvements for \emph{every} project in
Java projects from DaCapo benchmark, popular projects and uniformly
sampled projects from GitHub. For execution time, CPU usage, and memory
consumption, ARTEMIS finds at least one solution that improves \emph{all}
measures for () of the projects. The median improvement across
the best solutions is , , for runtime, memory and CPU
usage.
These aggregate results understate ARTEMIS's potential impact. Some of the
benchmarks it improves are libraries or utility functions. Two examples are
gson, a ubiquitous Java serialization framework, and xalan, Apache's XML
transformation tool. ARTEMIS improves gson by \%, and for
memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
\%. \emph{Every} client of these projects will benefit from these
performance improvements.Comment: 11 page
Train-scheduling optimization model for railway networks with multiplatform stations
This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version
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