2,690 research outputs found
Average Drift Analysis and Population Scalability
This paper aims to study how the population size affects the computation time
of evolutionary algorithms in a rigorous way. The computation time of an
evolutionary algorithm can be measured by either the expected number of
generations (hitting time) or the expected number of fitness evaluations
(running time) to find an optimal solution. Population scalability is the ratio
of the expected hitting time between a benchmark algorithm and an algorithm
using a larger population size. Average drift analysis is presented for
comparing the expected hitting time of two algorithms and estimating lower and
upper bounds on population scalability. Several intuitive beliefs are
rigorously analysed. It is prove that (1) using a population sometimes
increases rather than decreases the expected hitting time; (2) using a
population cannot shorten the expected running time of any elitist evolutionary
algorithm on unimodal functions in terms of the time-fitness landscape, but
this is not true in terms of the distance-based fitness landscape; (3) using a
population cannot always reduce the expected running time on fully-deceptive
functions, which depends on the benchmark algorithm using elitist selection or
random selection
On the Easiest and Hardest Fitness Functions
The hardness of fitness functions is an important research topic in the field
of evolutionary computation. In theory, the study can help understanding the
ability of evolutionary algorithms. In practice, the study may provide a
guideline to the design of benchmarks. The aim of this paper is to answer the
following research questions: Given a fitness function class, which functions
are the easiest with respect to an evolutionary algorithm? Which are the
hardest? How are these functions constructed? The paper provides theoretical
answers to these questions. The easiest and hardest fitness functions are
constructed for an elitist (1+1) evolutionary algorithm to maximise a class of
fitness functions with the same optima. It is demonstrated that the unimodal
functions are the easiest and deceptive functions are the hardest in terms of
the time-fitness landscape. The paper also reveals that the easiest fitness
function to one algorithm may become the hardest to another algorithm, and vice
versa
A Comparison of GAs Penalizing Infeasible Solutions and Repairing Infeasible Solutions on the 0-1 Knapsack Problem
Constraints exist in almost every optimization problem. Different
constraint handling techniques have been incorporated with genetic
algorithms (GAs), however most of current studies are based on
computer experiments. An example is Michalewicz\u27s comparison among
GAs using different constraint handling techniques on the 0-1
knapsack problem. The following phenomena are observed in
experiments: 1) the penalty method needs more generations to find a
feasible solution to the restrictive capacity knapsack than the
repair method; 2) the penalty method can find
better solutions to the average capacity knapsack. Such observations
need a theoretical explanation. This paper aims at providing a
theoretical analysis of Michalewicz\u27s experiments. The main result
of the paper is that GAs using the repair method are more efficient
than GAs using the penalty method on both restrictive capacity and
average capacity knapsack problems. This result of the average
capacity is a little different from Michalewicz\u27s experimental
results. So a supplemental experiment is implemented to support the
theoretical claim. The results confirm the general principle pointed
out by Coello: a better constraint-handling approach should tend to
exploit specific domain knowledge
Fast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels
The fitness level method is an easy-to-use tool for estimating the hitting
time of elitist EAs. Recently, general linear lower and upper bounds from
fitness levels have been constructed. However, the construction of these bounds
requires recursive computation, which makes them difficult to use in practice.
We address this shortcoming with a new directed graph (digraph) method that
does not require recursive computation and significantly simplifies the
calculation of coefficients in linear bounds. In this method, an EA is modeled
as a Markov chain on a digraph. Lower and upper bounds are directly calculated
using conditional transition probabilities on the digraph. This digraph method
provides straightforward and explicit expressions of lower and upper time bound
for elitist EAs. In particular, it can be used to derive tight lower bound on
both fitness landscapes without and with shortcuts. This is demonstrated
through four examples: the (1+1) EA on OneMax, FullyDeceptive, TwoMax1 and
Deceptive. Our work extends the fitness level method from addressing simple
fitness functions without shortcuts to more realistic functions with shortcuts
Transport properties of dense deuterium-tritium plasmas
Consistent descriptions of the equation of states, and information about
transport coefficients of deuterium-tritium mixture are demonstrated through
quantum molecular dynamic (QMD) simulations (up to a density of 600 g/cm
and a temperature of eV). Diffusion coefficients and viscosity are
compared with one component plasma model in different regimes from the strong
coupled to the kinetic one. Electronic and radiative transport coefficients,
which are compared with models currently used in hydrodynamic simulations of
inertial confinement fusion, are evaluated up to 800 eV. The Lorentz number is
also discussed from the highly degenerate to the intermediate region.Comment: 4 pages, 3 figure
A new framework for analysis of coevolutionary systems:Directed graph representation and random walks
Studying coevolutionary systems in the context of simplified models (i.e. games with pairwise interactions between coevolving solutions modelled as self plays) remains an open challenge since the rich underlying structures associated with pairwise comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problem that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modelled as a specific type of Markov chains ? random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provide the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled mannerauthorsversionPeer reviewe
Differential responses of two rubber tree clones to chilling stress
Chilling stress is one of the most important environmental factors that limit the growth, distribution and yield of rubber tree in China. The effects of chilling stress on the grated plants of two rubber trees clones, GT1 and Wenchang217, were studied by physiological methods in controlled light chamber in order to explore the physiological mechanism of cold tolerance in rubber tree. Our results show a significant change in the tested physiological parameters after chilling treatment between two rubber clones. In comparison with the case of rubber tree clone GT1, the level of malondialdehyde (MDA) increased while superoxide dismutase, especially peroxidase and catalase decreased significantly in the seedlings of rubber tree clone Wenchang217 in response to chilling stress. As the cold tolerance ability of rubber tree clone GT1 is stronger than that of rubber tree clone Wenchang217, activation of oxidative quenching enzyme system should be one of the important factors that determine the cold tolerance of rubber tree.Keywords: Chilling stress, cold tolerance, Hevea brasiliensis Muell. Arg., physiological parameter, seedlin
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