Never mind the iPhone X, battery life could soon take a great leap forward

First paragraph: Another suite of Apple iPhones, another media frenzy. Much has been written about the $999/£999 iPhone X, the demise of the home button, the “face ID” function, wireless charging and so on. Somewhere down the list of improvements was extra battery life, at least for the iPhone X, thanks to its new souped up A11 bionic processor

Why we fell out of love with algorithms inspired by nature

First paragraph: While computers are poor at creativity, they are adept at crunching through vast numbers of solutions to modern problems where there are numerous complex variables at play. Take the question of finding the best delivery plan for a distribution company – where best to begin? How many vehicles? Which stretches of road need to be avoided at which times? If you want to get close to a sensible answer, you need to ask a computer. Access this article on The Conversation website: https://theconversation.com/why-we-fell-out-of-love-with-algorithms-inspired-by-nature-4271

Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming

Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improvement them.  These methods form a continuum of methodologies, from the “limited” ability to change (for example only the parameters) to the “complete” ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH) to automatically design algorithms, and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approache

Search-based energy optimization of some ubiquitous algorithms

Reducing computational energy consumption is of growing importance, particularly at the extremes (i.e. mobile devices and datacentres). Despite the ubiquity of the JavaTM Virtual Machine (JVM), very little work has been done to apply Search Based Software Engineering (SBSE) to minimize the energy consumption of programs that run on it. We describe OPACITOR , a tool for measuring the energy consumption of JVM programs using a bytecode level model of energy cost. This has several advantages over time-based energy approximations or hardware measurements. It is: deterministic.  unaffected by the rest of the computational environment.  able to detect small changes in execution profile, making it highly amenable to metaheuristic search which requires locality of representation. We show how generic SBSE approaches coupled with OPACITOR achieve substantial energy savings for three widely-used software components. Multi-Layer Perceptron implementations minimis- ing both energy and error were found, and energy reductions of up to 70% and 39.85% were obtained over the original code for Quicksort and Object-Oriented container classes respectively. These highlight three important considerations for automatically reducing computational energy: tuning software to particular distributions of data; trading off energy use against functional properties; and handling internal dependencies which can exist within software that render simple sweeps over program variants sub-optimal. Against these, global search greatly simplifies the developer’s job, freeing development time for other tasks

Metaheuristic Design Pattern: Surrogate Fitness Functions

Certain problems have characteristics that present difficulties for metaheuristics: their objective function may be either prohibitively expensive, or they may only give a partial ordering over the solutions, lacking a suitable gradient to guide the search. In such cases, it may be more efficient to use a surrogate fitness function to replace or supplement the objective function. This paper provides a broad perspective on surrogate fitness functions, described in the form of a metaheuristic design pattern

Air traffic control about to let pilots plan their own routes – but don’t worry

First paragraph: A major change is coming to our skies. From next March, pilots willbe able todetermine their own routes and plan to fly direct from point to point.  Access article on The Conversation website here: https://theconversation.com/air-traffic-control-about-to-let-pilots-plan-their-own-routes-but-dont-worry-3544

GP vs GI: if you can't beat them, join them

Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is also true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with existing software, and therefore immediately has the aim of tackling real software. As evolution is the main approach to GI to manipulating programs, this connection with real software should persuade the GP community to confront the issues around what it originally set out to tackle i.e. evolving real software

Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces

Brain Computer Interfaces provide a very challenging classification task due to small numbers of instances, large numbers of features, non-stationary problems, and low signal-to-noise ratios. Feature selection (FS) is a promising solution to help mitigate these effects. Wrapper FS methods are typically found to outperform filter FS methods, but reliance on cross-validation accuracies can be misleading due to overfitting. This paper proposes a filter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature

Software Improvement with Gin: A Case Study

We provide a case study for the usage of Gin, a genetic improvement toolbox for Java. In particular, we implemented a simple GP search and targeted two software optimisation properties: runtime and repair. We ran our search algorithm on Gson, a Java library for converting Java objects to JSON and vice-versa. We report on runtime improvements and fixes found. We provide all the new code and data on the dedicated website: https://github.com/justynapt/ssbseChallenge2019

Evals is not enough: why we should report wall-clock time

Have you ever noticed that your car never achieves the fuel economy claimed by the manufacturer? Does this seem unfair? Unscientific? Would you like the same situation to occur in Genetic Improvement? Comparison will always be difficult [9], however, guidelines have been discussed [3, 5, 4]. With two GP [8] approaches, comparing the number of evaluations of the fitness function is reasonably fair. This means you are comparing the GP systems, and not how well they are implemented, how fast the language is. However, the situation with GI [6, 1] is unique. With GI we will typically compare systems which are applied to the same application written in the same language (i.e. a GI systems targeted at Java, may not even be applied to C). Thus, wall-clock time becomes more relevant. Thus, this paper asks if reporting number of evaluations is enough, or if wall-clock time is also important, particularly in the context of GI. It argues that reporting time is even more important when doing GI when compared to traditional GP