291,604 research outputs found
Evolutionary genetics and dynamics of transitions in sex determination
Sex determination (SD) is an essential part of an individual's development, but the mechanisms controlling SD are incredibly variable and subject to rapid evolutionary change. When new SD genes evolve, this can lead to the formation of sex chromosomes. Sex chromosomes evolve from regular autosomes into a pair of highly-differentiated chromosomes via sex-specific adaptation and genetic degeneration. The evolutionary causes and consequences of SD evolution have been intensively studied using theoretical approaches, but many questions remain. Existing models of SD evolution predict that multiple SD genes generally cannot coexist, while some such systems occur in amongst others insects and amphibians. Here, several models are developed that may help explain why complex SD systems may persist over evolutionary time scales. This is done by assuming that the genes controlling SD do not act in isolation, but instead that SD as a process is integrated into and affected by the biology and ecology of the organism. Early sex chromosome evolution is thought to occur by sex-specific adaptation and the evolution of recombination suppression. Testing this theory has proven difficult as in absence of recombination, sex chromosomes are prone to genetic decay and mutation accumulation. Here, the complex SD system of the housefly Musca domestica is exploited to generate new sex chromosomes, which circumvent the issue of chromosomal decay and may be used to study the early stages of sex chromosome evolution. Further development of the housefly as a model system for evolutionary studies will be needed to bring this potential to full fruition. Altogether, these findings show that SD, and the mechanisms controlling it, is more complex then commonly considered
Using Evolutionary Mutation Testing to improve the quality of test suites
Mutation testing is a method used to assess and improve the fault detection capability of a test suite by creating faulty versions, called mutants, of the system under test. Evolutionary Mutation Testing (EMT), like selective mutation or mutant sampling, was proposed to reduce the computational cost, which is a major concern when applying mutation testing. This technique implements an evolutionary algorithm to produce a reduced subset of mutants but with a high proportion of mutants that can help the tester derive new test cases (strong mutants). In this paper, we go a step further in estimating the ability of this technique to induce the generation of test cases. Instead of measuring the percentage of strong mutants within the subset of generated mutants, we compute how much the test suite is actually improved thanks to those mutants. In our experiments, we have compared the extent to which EMT and the random selection of mutants help to find missing test cases in C++ object-oriented systems. We can conclude from our results that the percentage of mutants generated with EMT is lower than with the random strategy to obtain a test suite of the same size and that the technique scales better for complex programs
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities
Algorithmic complexity vulnerabilities occur when the worst-case time/space
complexity of an application is significantly higher than the respective
average case for particular user-controlled inputs. When such conditions are
met, an attacker can launch Denial-of-Service attacks against a vulnerable
application by providing inputs that trigger the worst-case behavior. Such
attacks have been known to have serious effects on production systems, take
down entire websites, or lead to bypasses of Web Application Firewalls.
Unfortunately, existing detection mechanisms for algorithmic complexity
vulnerabilities are domain-specific and often require significant manual
effort. In this paper, we design, implement, and evaluate SlowFuzz, a
domain-independent framework for automatically finding algorithmic complexity
vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case
algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided
evolutionary search techniques to automatically find inputs that maximize
computational resource utilization for a given application.Comment: ACM CCS '17, October 30-November 3, 2017, Dallas, TX, US
How to shift bias: Lessons from the Baldwin effect
An inductive learning algorithm takes a set of data as input and generates a hypothesis as
output. A set of data is typically consistent with an infinite number of hypotheses;
therefore, there must be factors other than the data that determine the output of the
learning algorithm. In machine learning, these other factors are called the bias of the
learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently
developed learning algorithms dynamically adjust their bias as they search for a
hypothesis. Algorithms that shift bias in this manner are not as well understood as
classical algorithms. In this paper, we show that the Baldwin effect has implications for
the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in
1896, to explain how phenomena that might appear to require Lamarckian evolution
(inheritance of acquired characteristics) can arise from purely Darwinian evolution.
Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We
explore a variation on their model, which we constructed explicitly to illustrate the lessons
that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that
it appears that a good strategy for shift of bias in a learning algorithm is to begin with a
weak bias and gradually shift to a strong bias
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