Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 140

This bibliography lists 306 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1975

Automated Design and Optimization of Metallic Alloys

The design and optimization of metallic alloys poses a significant engineering challenge. The search space of all possible alloys is sufficiently large that it is impossible to fully explore by traditional methods. In order to address this challenge, physics based computational frameworks linked to advanced machine learning algorithms can serve to automate this process with computational efficiency such that the state of the industry may be rapidly advanced. The work herein presents a suite of computational frameworks leveraged to automate the design and optimization process of advanced alloys. An ab initio alloy thermodynamics system, Molecular Dynamics simulations, a Convolutional-Neural Network system, and a coupled Neural Network and Multi-objective Genetic Algorithm. These algorithms are validated over the set of binary nanocrystalline Al-X alloys, and multi-component High Entropy Alloys (HEA)

Genetic Operators Applied to Symmetric Cryptography

In this article, a symmetric-key cryptographic algorithm for text is proposed, which applies Genetic Algorithms philosophy, entropy and modular arithmetic. An experimental methodology is used over a deterministic system, which redistributes and modifies the parameters and phases of the genetic algorithm that directly affect its behavior, carrying out a constant evaluation using the fitness function, in order to optimize the results. An independent encryption is established for the auxiliary key, using a main key, in charge of increasing security. The tests are performed over different text sizes, manipulating the parameters and criteria proposed to obtain their appropriate values. Finally, a comparison is presented against the following cryptographic algorithms DES (Data Encryption Standard), RSA (Rivest, Shamir and Adleman) and AES (Advanced Encryption Standard), exposing factors such as processing time, scalability, key size, etc. It is shown that the proposed algorithm has a better performance

Multi-objective genetic algorithms in the study of the genetic code’s adaptability

Using a robustness measure based on values of the polar requirement of amino acids, Freeland and Hurst (1998) showed that less than one in one million random hypothetical codes are better than the standard genetic code. In this paper, instead of comparing the standard code with randomly generated codes, we use an optimisation algorithm to find the best hypothetical codes. This approach has been used before, but considering only one objective to be optimised. The robustness measure based on the polar requirement is considered the most effective objective to be optimised by the algorithm. We propose here that the polar requirement is not the only property to be considered when computing the robustness of the genetic code. We include the hydropathy index and molecular volume in the evaluation of the amino acids using three multi-objective approaches: the weighted formula, lexicographic and Pareto approaches. To our knowledge, this is the first work proposing multi-objective optimisation approaches with a non-restrictive encoding for studying the evolution of the genetic code. Our results indicate that multi-objective approaches considering the three amino acid properties obtain better results than those obtained by single objective approaches reported in the literature. The codes obtained by the multi-objective approach are more robust and structurally more similar to the standard code

Intelligent network intrusion detection using an evolutionary computation approach

With the enormous growth of users\u27 reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely remove the threat of network attacks. A good security mechanism requires an Intrusion Detection System (IDS) in order to monitor security breaches when the prevention schemes in the preparation phase are bypassed. To be able to react to network attacks as fast as possible, an automatic detection system is of paramount importance. The later an attack is detected, the less time network administrators have to update their signatures and reconfigure their detection and remediation systems. An IDS is a tool for monitoring the system with the aim of detecting and alerting intrusive activities in networks. These tools are classified into two major categories of signature-based and anomaly-based. A signature-based IDS stores the signature of known attacks in a database and discovers occurrences of attacks by monitoring and comparing each communication in the network against the database of signatures. On the other hand, mechanisms that deploy anomaly detection have a model of normal behaviour of system and any significant deviation from this model is reported as anomaly. This thesis aims at addressing the major issues in the process of developing signature based IDSs. These are: i) their dependency on experts to create signatures, ii) the complexity of their models, iii) the inflexibility of their models, and iv) their inability to adapt to the changes in the real environment and detect new attacks. To meet the requirements of a good IDS, computational intelligence methods have attracted considerable interest from the research community. This thesis explores a solution to automatically generate compact rulesets for network intrusion detection utilising evolutionary computation techniques. The proposed framework is called ESR-NID (Evolving Statistical Rulesets for Network Intrusion Detection). Using an interval-based structure, this method can be deployed for any continuous-valued input data. Therefore, by choosing appropriate statistical measures (i.e. continuous-valued features) of network trafc as the input to ESRNID, it can effectively detect varied types of attacks since it is not dependent on the signatures of network packets. In ESR-NID, several innovations in the genetic algorithm were developed to keep the ruleset small. A two-stage evaluation component in the evolutionary process takes the cooperation of rules into consideration and results into very compact, easily understood rulesets. The effectiveness of this approach is evaluated against several sources of data for both detection of normal and abnormal behaviour. The results are found to be comparable to those achieved using other machine learning methods from both categories of GA-based and non-GA-based methods. One of the significant advantages of ESR-NIS is that it can be tailored to specific problem domains and the characteristics of the dataset by the use of different fitness and performance functions. This makes the system a more flexible model compared to other learning techniques. Additionally, an IDS must adapt itself to the changing environment with the least amount of configurations. ESR-NID uses an incremental learning approach as new flow of traffic become available. The incremental learning approach benefits from less required storage because it only keeps the generated rules in its database. This is in contrast to the infinitely growing size of repository of raw training data required for traditional learning

Genetic learning of fuzzy reactive controllers

P. 33-41This paper concerns the learning of basic behaviors in an autonomous robot. It presents a method to adapt basic reactive behaviors using a genetic algorithm. Behaviors are implemented as fuzzy controllers and the genetic algorithm is used to evolve their rules. These rules will be formulated in a fuzzy way using prefixed linguistic labels. In order to test the rules obtained in each generation of the genetic evolution process, a real robot has been used. Numerical results from the evolution rate of the different experiments, as well as an example of the fuzzy rules obtained, are presented and discussedS