R. Muhammad Atif Azad

Birmingham City University | BCU · School of Computing and Digital Technology

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. A...

In genetic programming (GP), controlling complexity often means reducing the size of evolved expressions. However, previous studies show that size reduction may not avoid model overfitting. Therefore, in this study, we use the evaluation time --- the computational time required to evaluate a GP model on data --- as the estimate of model complexity....

There were over 70,000 drug overdose deaths in the USA in 2017. Almost half of those involved the use of Opioids such as Heroin. This research supports efforts to combat the Opioid Epidemic by further understanding factors that lead to Heroin consumption. Previous research has debated the cause of Heroin addiction, with some explaining the phenomen...

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary v...

This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain...

During the last few decades, the widespread growth of scholarly networks and digital libraries has resulted in an explosion of publicly available scholarly data in various forms such as authors, papers, citations, conferences, and journals. This has created interest in the domain of big scholarly data analysis that analyses worldwide dissemination...

In machine learning, reducing the complexity of a model can help to improve its computational efficiency and avoid overfitting. In genetic programming (GP), the model complexity reduction is often achieved by reducing the size of evolved expressions. However, previous studies have demonstrated that the expression size reduction does not necessarily...

Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation...

Background: Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning m...

There were over 70,000 drug overdose deaths in the USA in 2017. Almost half of those involved the use of Opioids such as Heroin. This research supports efforts to combat the Opioid Epidemic by further understanding factors that lead to Heroin consumption. Previous research has debated the cause of Heroin addiction, with some explaining the phenomen...

Isolating the fitness-contribution of substructures is typically a difficult task in Genetic Programming (GP). Hence, useful substructures are lost when the overall structure (model) performs poorly. Furthermore, while crossover is heavily used in GP, it typically produces offspring models with significantly lower fitness than that of the parents....

Complexity of evolving models in genetic programming (GP) can impact both the quality of the models and the evolutionary search. While previous studies have proposed several notions of GP model complexity, the size of a GP model is by far the most researched measure of model complexity. However, previous studies have also shown that controlling the...

Traditionally, reducing complexity in Machine Learning promises benefits such as less overfitting. However, complexity control in Genetic Programming (GP) often means reducing the sizes of the evolving expressions, and past literature shows that size reduction does not necessarily reduce overfitting. In fact, whether size consistently represents co...

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML...

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML...

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML...

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML...

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML...

Early detection of emerging research trends could potentially revolutionise the way research is done. For this reason, trend analysis has become an area of paramount importance in academia and industry. This is due to the significant implications for research funding and public policy. The literature presents several emerging approaches to detectin...

Research in analysis of big scholarly data has increased in the recent past and it aims to understand research dynamics and forecast research trends. The ultimate objective in this research is to design and implement novel and scalable methods for extracting knowledge and computational history. While citations are highly used to identify emerging/r...

Although some of the earliest Estimation of Distribution Algorithms (EDAs) utilized bivariate marginal distribution models, up to now, all discrete bivariate EDAs had one serious limitation: they were constrained to exploiting only a limited O(d) subset out of all possible O(d^2) bivari-ate dependencies. As a first we present a family of discrete b...

Word embeddings are increasingly attracting the attention of researchers dealing with semantic similarity and analogy tasks. However, finding the optimal hyper-parameters remains an important challenge due to the resulting impact on the revealed analogies mainly for domain-specific corpora. While analogies are highly used for hypotheses synthesis,...

This chapter evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results by comparing against those from a reasonably standard Genetic Programming (GP) setup. Specifically, the chapter compares a standard GE method to constant creation termed digit concatenation with what this chapter c...

Multi-cores offer higher processing power than single core processors. However, as the number of cores available on a single processor increases, efficiently programming them becomes increasingly more complex, often to the point where the limiting factor in speeding up tasks is the software. We present Grammatical Automatic Parallel Programming (GA...

In this paper, we propose a hybrid approach to solving multi-class problems which combines evolutionary computation with elements of traditional machine learning. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolu...

A new family of Estimation of Distribution Algorithms (EDAs) for discrete search spaces is presented. The proposed algorithms, which we label DICE (Discrete Correlated Estimation of distribution algorithms) are based, like previous bivariate EDAs such as MIMIC and BMDA, on bivariate marginal distribution models. However, bivariate models previously...

Wave is a novel form of semantic genetic programming which operates by optimising the residual errors of a succession of short genetic programming runs, and then producing a cumulative solution. These short genetic programming runs are called periods, and they have heterogeneous parameters. In this paper we leverage the potential of Wave's heteroge...

We introduce a new approach to the principled design of evolutionary algorithms (EAs) based on kernel methods. We demonstrate how kernel functions, which capture useful problem domain knowledge, can be used to directly construct EA search operators. We test two kernel search operators on a suite of four challenging combinatorial optimization proble...

This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With mino...

Typically, Genetic Programming (GP) attempts to solve a problem by evolving solutions over a large, and usually pre-determined number of generations. However, overwhelming evidence shows that not only does the rate of performance improvement drop considerably after a few early generations, but that further improvement also comes at a considerable c...

Writing parallel programs is a challenging but unavoidable proposition to take true advantage of multi-core processors. In this paper, we extend Multi-core Grammatical Evolution for Parallel Sorting (MCGE-PS) to evolve parallel iterative sorting algorithms while also optimizing their degree of parallelism. We use evolution to optimize the performa...

This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short, and dependent but potentially heterogeneous GP runs provides a collective solution ; the sequence akins a wave such that each short GP run is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as popul...

Although Evolutionary Computation (EC) has been used with considerable success to evolve computer programs, the majority of this work has targeted the production of serial code. Recent work with Grammatical Evolution (GE) produced Multi-core Grammatical Evolution (MCGE-II), a system that natively produces parallel code, including the ability to exe...

Increasing availability of multiple processing elements on the recent desktop and personal computers poses unavoidable challenges in realizing their processing power. The challenges include programming these high processing elements. Parallel programming is an apt solution for such a realization of the computational capacity. However, it has many d...

Writing recursive programs for fine-grained task-level execution on parallel architectures, such as the current generation of multi-core machines, often require the application of skilled parallelization knowledge to fully realize the potential of the hardware. This paper automates the process by using Grammatical Evolution (GE) to exploit the mu...

Sorting algorithms that offer the potential for data-parallel execution on parallel architectures are an excellent tool for the current generation of multi-core processors that often require skilled parallelization knowledge to fully realize the potential of the hardware. We propose to automate the evolution of natively parallel programs us- ing...

The ability to generalize beyond the training set is paramount for any machine learning algorithm and Genetic Programming (GP) is no exception. This paper investigates a recently proposed technique to improve generalisation in GP, termed Interleaved Sampling where GP alternates between using the entire data set and only a single data point in alter...

Local search methods can harmoniously work with global search methods such as Evolutionary Algorithms (EAs); however, particularly in Genetic Programming (GP), concerns remain about the additional cost of local search (LS). One successful such system is Chameleon, which tunes internal GP nodes and addresses cost concerns by employing a number of st...

The quality of candidate solutions in evolutionary computation (EC) depend on multiple independent runs and a large number of them fail to guarantee optimal result. These runs consume more or less equal or sometimes higher amount of computational resources on par the runs that produce desirable results. This research work addresses these two issue...

The quality of the evolved solutions of an evolutionary algorithm (EA) varies across different runs and a significant percentage of runs can produce solutions of undesirable quality. These runs are a waste of computational resources, particularly in difficult problems where practitioners have time bound limitations in repeating runs. This paper pr...

We describe the utilization of on-chip multiple CPU architectures to automatically evolve parallel computer programs. These programs have the capability of exploiting the computational efficiency of the modern multi-core machines. This is significantly different from other parallel EC approaches because not only do we produce individuals that, in...

The ability to generalize beyond the training set is important for Genetic Programming (GP). Interleaved Sampling is a recently proposed approach to improve generalization in GP. In this technique, GP alternates between using the entire data set and only a single data point. Initial results showed that the technique not only produces solutions that...

This paper evaluates the performance of various methods to constant creation in Grammatical Evolution (GE), and validates the results against those from Genetic Programming (GP). Constant creation in GE is an important issue due to the disruptive nature of ripple crossover, which can radically remap multiple terminals in an individual, and we inves...

Abstract Genetic Programming (GP) coarsely models natural evolution to evolve computer programs. Unlike in nature, where individuals can often improve their fitness through lifetime experience, the fitness of GP individuals generally does not change during their lifetime, and there is usually no opportunity to pass on acquired knowledge. This paper...

Historically, the quality of a solution in Genetic Programming (GP) was often assessed based on its performance on a given training sample. However, in Machine Learning, we are more interested in achieving reliable estimates of the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data du...

Typically, the quality of a solution in Genetic Programming (GP) is represented by a score on a given training sample. However, in Machine Learning, we are most interested in estimating the quality of the evolving individuals on unseen data. In this paper, we propose to simulate the effect of unseen data to direct training without actually using ad...

Computational complexity analysis on Evolutionary Algorithms can provide crucial insight into how they work. While relatively straight forward for fixed length structures, it is less so for variable length structures, although initial work has already been conducted on tree based Genetic Programming (GP) algorithms. Grammatical Evolution (GE) is a...

Estimating the quality of Voice over Internet Protocol (VoIP) as perceived by humans is considered a formidable task. This is partly due to the relatively large number of variables that are involved as determinants of quality. Moreover, discerning the significance of one variable over the other is difficult. In this paper a novel approach based on...

Recent work has enhanced the Evolutionary Bayesian Classifier-based Optimization Algorithm (EBCOA) by oversampling the next generation and identifying promising solutions without actually evaluating their fitness values. In order to model the existing generation, that work considered two classes of solutions, that is, high performing solutions (H-G...

This paper proposes to improve the performance of Genetic Programming (GP) over unseen data by minimizing the variance of the output values of evolving models alongwith reducing error on the training data. Variance is a well understood, simple and inexpensive statistical measure; it is easy to integrate into a GP implementation and can be computed...

Typically, an individual in Genetic Programming (GP) can not make the most of its genetic inheritance. Once it is mapped, its fitness is immediately evaluated and it survives only until the genetic operators and its competitors eliminate it. Thus, the key to survival is to be born strong. This paper proposes a simple alternative to this powerlessne...

This paper proposes a novel approach to quantifying the quality degradation of Voice over IP (VoIP) telephony in the presence of codec and network-related impairments. This approach differs from the baisc ITU-T E-Model for VoIP quality estimation in that it addresses mixed narrowband/wideband scenarios. It makes novel use of instrumental models and...

We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural networks in more depth with flexibility. SSNNS is based on the Spike Response ...

Voice over IP (VoIP) speech quality estimation is crucial to providing optimal Quality of Service (QoS). This paper seeks to provide improved speech quality estimation models with better prediction accuracy by considering a richer set of input features than the current International Telecom- munications Union-Telecommunication (ITU-T) recommen- dat...

In this paper we have employed a genetic programming (GP) based symbolic regression approach to estimate the speech quality as a function of impairments due to IP network and low bitrate coding. A main advantage of GP is that it can produce human-readable results in the form of analytical expressions. Moreover, GP is capable of weeding out irreleva...

Speech quality, as perceived by the users of Voice over In- ternet Protocol (VoIP) telephony, is critically important to the uptake of this service. VoIP quality can be degraded by network layer problems (delay, jitter, packet loss). This paper presents a method for real-time, non-intrusive speech quality estimation for VoIP that emulates the sub-...

Packet loss is bursty in nature and it affects the quality of VoIP adversely. In this article a novel approach based on Genetic Programming (GP) has been presented that maps the effect of bursty packet loss on listeners' perception of speech quality. The ITU-T P.862 (PESQ) algorithm is used as a reference model in this research. A simplistic non-li...

This chapter examines the notion of co-evolving grammars with a population of individuals. This idea has great promise because it is possible to dynamically reshape the solution space while evolving individuals. We compare such a system with a more standard system with fixed grammars and demonstrate that, on a selection of benchmark problems, the s...

In this paper we introduce a cooperative revolutionary algorithm based on the ideas of endosymbiosis. We compare it to a generational GA on two deceptive and decomposable problems and show that it has better scaling properties as the problem size increases. We then analyse what effect crossover and parasite mutation has on its performance and concl...

This paper presents a new migration strategy that improves the overall quality of solutions in a distributed genetic algorithm (DGA) involving a number of concurrently evolving populations. The idea behind this improvement is to incorporate a diversity guided selection mechanism that selects a diverse set of individuals for migration from the evolv...

We propose a new methodology to look at the fitness contributions (semantics) of different schemata in Genetic Programming (GP). We hypothesize that the significance of a schema can be evaluated by calculating its fitness contribution to the total fitness of the trees that contain it, and use our methodology to test this hypothesis.It is shown that...

This paper is concerned with examining the way in which rooted building blocks grow in GP-like systems. We hypothesize that, in addition to the normal notion of co-operative building blocks, there are also competitive building blocks in the population. These competitive building blocks are all of the rooted variety, all share a similar root struc-...

Restoring the blood supply to a diseased artery is achieved by using a vascular bypass graft. The surgical procedure is a well documented and successful technique. The most commonly cited hemodynamic factor implicated in the disease initiation and proliferation processes at graft/artery junctions is Wall Shear Stress (WSS). WSS distributions are pr...

Many evolutionary systems have been developed that solve various specific scheduling problems. In this work, one such permutation based system, which uses a linear GP type Genotype to Phenotype Mapping (GPM), known as the Random Key Genetic Algorithm is investigated. The role standard mutation plays in this representation is analysed formally and i...

This paper compares two grammar based Evolutionary Automatic Programming methods, Grammatical Evolution (GE) and Chorus. Both systems evolve sequences of derivation rules which can be used to produce computer programs, however, Chorus employs a position independent representation, while GE uses polymorphic codons, the meaning of which depends on th...

One of the key characteristics of EvolutionaryAlgorithms is the manner in which solutions are evolved from a primordial soup. Theway this soup, or initial generation, is created can have major implications for the eventual quality of the search, as, if there is not enough diversity, the population may become stuck on a local optimum. This paper rep...

We describe a new encoding system, Chorus, for grammar based Evolutionary Algorithms. This scheme is coarsely based on the manner in nature in which genes produce proteins that regulate the metabolic pathways of the cell. The phenotype is the behaviour of the cells metabolism, which corresponds to the development of the computer program in our case...