Geva

O’Neill, Michael; Hemberg, Erik; Gilligan, Conor; Bartley, Eliott; McDermott, James; Brabazon, Anthony

doi:10.1145/1527063.1527066

Cited by 65 publications

(7 citation statements)

References 16 publications

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“…Grammatical Evolution [23,24,21,11] is an evolutionary technique that is able to represent individuals by means of grammars. GE is a form of Genetic Programming (GP) [17] which evolves programs that are evaluated according their fitness values.…”

Section: Grammatical Evolutionmentioning

confidence: 99%

Optimizing L1 cache for embedded systems through grammatical evolution

Álvarez,

Colmenar,

Risco-Martín

et al. 2023

Preprint

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Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers face to a wide and time-consuming exploration space.

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Section: Grammatical Evolutionmentioning

confidence: 99%

Optimizing L1 cache for embedded systems through grammatical evolution

Álvarez,

Colmenar,

Risco-Martín

et al. 2023

Preprint

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“…The genotype or phenotype (e.g., is a part of genotype) mapping indicates that in lieu of operating particularly on solution trees, as in the standard GP, the GE permits search operators to be executed on the genotype (e.g., binary or integer genes), moreover, partially resulting phenotypes, and the wholly formed phenotypic derivation trees themselves. One of the advantages of GE is that this mapping explains the use of search to various programming languages and other structures [83], [100], [116], [117]. Table 5 summarizes some applications of the GEGP.…”

Section: Grammatical Evolution Genetic Programming (Gegp)mentioning

confidence: 99%

“…Type of GP Advantages Disadvantages Tree-based (TGP) [77], [100], [103]  Higher-order functions are a powerful addition to the TGP which enables the evolution of programs with greater than constant-time complexity  Closure (having the same data type between operators and terminals), which causes to increase the AC in the multiple data type problems  High AC in the Lawnmower and H-IFF problems Stack-based (SGP) [80], [94], [95], [113], [114], [120]  High performance on symbolic regression problem  Low AC (outperforms the TGP)  Efficient performance in parallel computing  Inefficient performance where long programs (variables) are pushed in the stack on limited resources systems  It can only be implemented on stack support languages. Linear (LGP) [81]  High flexibility (e.g., allows more freedom on the internal representation)  Low AC  Allowing a more efficient evaluation of programs  Higher compiler overhead than the TGP Grammatical Evolution (GEGP) [78], [83], [100], [121], [124]  The flexibility of language choice that it allows (e.g. the user could output algorithm in any language and utilize a compiler for that language to write an executable code to calculate a fitness).…”

Section: Table 10 Advantages and Disadvantages Of Various Types Of Gpsmentioning

confidence: 99%

A Survey of Genetic Programming and Its Applications

Ahvanooey¹,

Li²,

Wu³

et al. 2019

KSII TIIS

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Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively rewriting them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

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“…To implement our GEA, we have used GEVA [37], a well-known GE tool developed in Java. The distributed pGEA version is made by adding the DEVS/SOA framework, which will use multiple processors when available.…”

Section: Case Studiesmentioning

confidence: 99%

A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems

et al. 2010

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a b s t r a c tFor the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40Â when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two wellknown general-purpose DMMs and two state-of-the-art optimization methodologies.

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Geva

Abstract: Publication informationSIGEVOLution, 3 (2): 17-22Publisher ACM Link to online version http://dx

Cited by 65 publications

References 16 publications

Optimizing L1 cache for embedded systems through grammatical evolution

Optimizing L1 cache for embedded systems through grammatical evolution

A Survey of Genetic Programming and Its Applications

A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems

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