Multitask Evolution with Cartesian Genetic Programming

We introduce a genetic programming method for solving multiple Boolean circuit synthesis tasks simultaneously. This allows us to solve a set of elementary logic functions twice as easily as with a direct, single-task approach.Comment: 2 page

Evolving Graphs by Graph Programming

Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search

OCEAn: Ordinal classification with an ensemble approach

Generally, classification problems catalog instances according to their target variable with out considering the relation among the different labels. However, there are real problems in which the different values of the class are related to each other. Because of interest in this type of problem, several solutions have been proposed, such as cost-sensitive classi fiers. Ensembles have proven to be very effective for classification tasks; however, as far as we know, there are no proposals that use a genetic-based methodology as the meta heuristic to create the models. In this paper, we present OCEAn, an ordinal classification algorithm based on an ensemble approach, which makes a final prediction according to a weighted vote system. This weighted voting takes into account weights obtained by a genetic algorithm that tries to minimize the cost of classification. To test the performance of this approach, we compared our proposal with ordinal classification algorithms in the literature and demonstrated that, indeed, our approach improves on previous resultsMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-126334

Information Processor

How computational technology start to take place and gradually become being heavily involved/implemented in the design process of architectural design. In the architecture domain, not only the proportion of the assistance from computational techniques has been increasing exponentially, but also, the role they play has been gradually shifting from a supporting one to a generative one. No longer limited to being a complex mathematics calculator, computers, have become a ubiquitous necessity in our daily life and even influence the way we live. This, is especially true for the young generation who were born in this digital world, mainly referred to as the “Generation Z”. Business Insider, a fast-growing business media website, mentioned that “Gen Z-ers are digitally over-connected. They multitask across at least five screens daily and spend 41% of their time outside of school with computers or mobile devices, compared to 22% 10 years ago, according to theSparks & Honey report.” When Alan Turing first invented the room-sized “Turing Machine” to decipher Nazi codes, he couldn’t have expected that this giant machine could one day be put into one’s pocket and efficiently compute a million times more data. As compared to the era of tools, such as paper and pen, the computer, in today’s context has been heavily utilized and relied upon as a powerful instrument. This change is remarkable, considering the relatively short period of time, especially after 1981 when the first IBM personal computer was released (Mitchell, 1990). Architecture Design cannot be excluded from this inevitable technological tendency. Even the most conservative architecture firms are now required to deliver digital technical drawings to communicate amongst designers, clients, and construction firms in the present scenario. Incorporating computer technology in today’s context also provides young designers the opportunity to experiment with creating relatively complex geometry based architectural space. But before applying this powerful technology in architectural design, the crucial knowledge behind it that architects had to understand and realize was the manner and procedure of “Processing of Information”. Without information, the computer would be just lying on one’s desk as a useless cube, like a vehicle without a driver, or a body without a soul. The shifting roles of computer technology in architectural design are obviously defined by the manner of how designers interpret, digest and operate/process the streams of information flow