Darwin: A Ground Truth Agnostic Captcha Generator Using Evolutionary Algorithm, Eric Y. Chen, Lin-Shung Huang, Ole J. Mengshoel, Jason D. Lohn
Jun 2014
Darwin: A Ground Truth Agnostic Captcha Generator Using Evolutionary Algorithm, Eric Y. Chen, Lin-Shung Huang, Ole J. Mengshoel, Jason D. Lohn
Ole J Mengshoel
We designed and implemented Darwin, the first CAPTCHA generator using evolutionary algorithm. We evaluated the effectiveness of our proposed CAPTCHAs with MTurk users (non-attackers) and Antigate workers (attackers). Due to our ground-truth agnostic fitness function, we are able to discover a new category of CAPTCHAs in which attackers answer correctly but non-attackers answer incorrectly.
Feedback Control For Multi-Modal Optimization Using Genetic Algorithms, Jun Shi, Ole J. Mengshoel, Dipan K. Pal
Jun 2014
Feedback Control For Multi-Modal Optimization Using Genetic Algorithms, Jun Shi, Ole J. Mengshoel, Dipan K. Pal
Ole J Mengshoel
Many optimization problems are multi-modal. In certain cases, we are interested in finding multiple locally optimal solutions rather than just a single optimum as is computed by traditional genetic algorithms (GAs). Several niching techniques have been developed that seek to find multiple such local optima. These techniques, which include sharing and crowding, are clearly powerful and useful. But they do not explicitly let the user control the number of local optima being computed, which we believe to be an important capability.
In this paper, we develop a method that provides, as an input parameter to niching, the desired number of …
Adaptive Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severinio Galan, Antonio De Dios
Dec 2013
Adaptive Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severinio Galan, Antonio De Dios
Ole J Mengshoel
The genetic algorithm technique known as crowding preserves population diversity by pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will survive (replacement phase). The replacement phase of crowding is usually carried out through deterministic or probabilistic crowding, which have the limitations that they apply the same selective pressure regardless of the problem being solved and the stage of genetic algorithm search. The recently developed generalized crowding approach introduces a scaling factor in the replacement phase, thus generalizing and potentially overcoming the limitations of both deterministic and probabilistic crowding. A …
A Novel Mating Approach For Genetic Algorithms, Severino Galan, Ole J. Mengshoel, Rafael Pinter
Dec 2012
A Novel Mating Approach For Genetic Algorithms, Severino Galan, Ole J. Mengshoel, Rafael Pinter
Ole J Mengshoel
Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called mating index that allows different mating strategies to be developed within a uniform framework: from an exploitative strategy called BEST-FIRST to an explorative one called BEST-LAST. SELF-ADAPTIVE mating is defined in the context of the novel algorithm …
Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan
Jun 2010
Generalized Crowding For Genetic Algorithms, Ole J. Mengshoel, Severino F. Galan
Ole J Mengshoel
Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will remain in the population (replacement phase). The present work focuses on the replacement phase of crowding, which usually has been carried out by one of the following three approaches: Deterministic, Probabilistic, and Simulated Annealing. These approaches present some limitations regarding the way replacement is conducted. On the one hand, the first two apply the same …