A Framework for Genetic Algorithms Based on Hadoop

Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in many real-world applications. The sequential execution of GAs requires considerable computational power both in time and resources. Nevertheless, GAs are naturally parallel and accessing a parallel platform such as Cloud is easy and cheap. Apache Hadoop is one of the common services that can be used for parallel applications. However, using Hadoop to develop a parallel version of GAs is not simple without facing its inner workings. Even though some sequential frameworks for GAs already exist, there is no framework supporting the development of GA applications that can be executed in parallel. In this paper is described a framework for parallel GAs on the Hadoop platform, following the paradigm of MapReduce. The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance. The framework has been also exploited to develop an application for Feature Subset Selection problem. A preliminary analysis of the performance of the developed GA application has been performed using three datasets and shown very promising performance

Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination

Machine Learning is a vital part of various modern day decision making software. At the same time, it has shown to exhibit bias, which can cause an unjust treatment of individuals and population groups. One method to achieve fairness in machine learning software is to provide individuals with the same degree of benefit, regardless of sensitive attributes (e.g., students receive the same grade, independent of their sex or race). However, there can be other attributes that one might want to discriminate against (e.g., students with homework should receive higher grades). We will call such attributes anti-protected attributes. When reducing the bias of machine learning software, one risks the loss of discriminatory behaviour of anti-protected attributes. To combat this, we use grid search to show that machine learning software can be debiased (e.g., reduce gender bias) while also improving the ability to discriminate against anti-protected attributes

The Effect of Offspring Population Size on NSGA-II: A Preliminary Study

Non-Dominated Sorting Genetic Algorithm (NSGA-II) is one of the most popular Multi-Objective Evolutionary Algorithms (MOEA) and has been applied to a large range of problems. Previous studies have shown that parameter tuning can improve NSGA-II performance. However, the tuning of the offspring population size, which guides the exploration-exploitation trade-off in NSGA-II, has been overlooked so far. Previous work has generally used the population size as the default offspring population size for NSGA-II. We therefore investigate the impact of offspring population size on the performance of NSGA-II. We carry out an empirical study by comparing the effectiveness of three configurations vs. the default NSGA-II configuration on six optimization problems based on four Pareto front quality indicators and statistical tests. Our findings show that the performance of NSGA-II can be improved by reducing the offspring population size and in turn increasing the number of generations. This leads to similar or statistically significant better results than those obtained by using the default NSGA-II configuration in 92% of the experiments performed

Multi-objective software effort estimation

We introduce a bi-objective effort estimation algorithm that combines Confidence Interval Analysis and assessment of Mean Absolute Error. We evaluate our proposed algorithm on three different alternative formulations, baseline comparators and current state-of-the-art effort estimators applied to five real-world datasets from the PROMISE repository, involving 724 different software projects in total. The results reveal that our algorithm outperforms the baseline, state-of-the-art and all three alternative formulations, statistically significantly (p < 0:001) and with large effect size (A12≥ 0:9) over all five datasets. We also provide evidence that our algorithm creates a new state-of-the-art, which lies within currently claimed industrial human-expert-based thresholds, thereby demonstrating that our findings have actionable conclusions for practicing software engineers

MODIFICAZIONI URBANE. ARCHITETTURE E CONNESSIONI A PALERMO

Il volume raccoglie gli esiti della ricerca e della didattica dei laboratori di Progettazione architettonica svolti presso il Dipartimento di Architettura dell\u2019Universit\ue0 degli Studi di Palermo, sul tema del progetto urbano. L\u2019obiettivo \ue8 quello di dimostrare come l\u2019esperienza di progetto sia capace di riaffermare la qualit\ue0 architettonica urbana nella citt\ue0 contemporanea. Inoltre, viene presentato un percorso di ricognizione e di conoscenza attraverso l\u2019elaborazione di progetti didattici, dal quale emerge una forte attenzione alle trasformazioni della citt\ue0.The book collects the results of the research and teaching of the architectural design laboratories carried out at the Department of Architecture of the University of Palermo, on the theme of the urban project. The goal is to demonstrate how the project experience is able to reaffirm the urban architectural quality in the contemporary city. In addition, a path of recognition and knowledge is presented through the development of educational projects, from which emerges a strong emphasis on the transformations of the city

Predictive analytics for software testing: Keynote paper

This keynote discusses the use of Predictive Analytics for Software Engineering, and in particular for Software Defect Prediction and Software Testing, by presenting the latest results achieved in these fields leveraging Artificial Intelligence, Search-based and Machine Learning methods, and by giving some directions for future work

Multi-Objective Software Effort Estimation: A Replication Study

Replication studies increase our confidence in previous results when the findings are similar each time, and help mature our knowledge by addressing both internal and external validity aspects. However, these studies are still rare in certain software engineering fields. In this paper, we replicate and extend a previous study, which denotes the current state-of-the-art for multi-objective software effort estimation, namely CoGEE. We investigate the original research questions with an independent implementation and the inclusion of a more robust baseline (LP4EE), carried out by the first author, who was not involved in the original study. Through this replication, we strengthen both the internal and external validity of the original study. We also answer two new research questions investigating the effectiveness of CoGEE by using four additional evolutionary algorithms (i.e., IBEA, MOCell, NSGA-III, SPEA2) and a well-known Java framework for evolutionary computation, namely JMetal (rather than the previously used R software), which allows us to strengthen the external validity of the original study. The results of our replication confirm that: (1) CoGEE outperforms both baseline and state-of-the-art benchmarks statistically significantly (p < 0:001); (2) CoGEE’s multi-objective nature makes it able to reach such a good performance; (3) CoGEE’s estimation errors lie within claimed industrial human-expert-based thresholds. Moreover, our new results show that the effectiveness of CoGEE is generally not limited to nor dependent on the choice of the multi-objective algorithm. Using CoGEE with either NSGA-II, NSGA-III, or MOCell produces human competitive results in less than a minute. The Java version of CoGEE has decreased the running time by over 99.8% with respect to its R counterpart. We have made publicly available the Java code of CoGEE to ease its adoption, as well as, the data used in this study in order to allow for future replication and extension of our work

Dynamical two-mode squeezing of thermal fluctuations in a cavity opto-mechanical system

We report the experimental observation of two-mode squeezing in the oscillation quadratures of a thermal micro-oscillator. This effect is obtained by parametric modulation of the optical spring in a cavity opto-mechanical system. In addition to stationary variance measurements, we describe the dynamic behavior in the regime of pulsed parametric excitation, showing enhanced squeezing effect surpassing the stationary 3dB limit. While the present experiment is in the classical regime, our technique can be exploited to produce entangled, macroscopic quantum opto-mechanical modes

Enhancing Genetic Improvement of Software with Regression Test Selection

Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 78% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area