Alienation Essay Example | Topics and Well Written Essays - 750 words

Alienation - Essay Example Most significantly, the novel depicts the unexpected entrance of Peter Walsh who was an old friend and former suitor of Clarissa. Their meeting reflects a mixture of happiness and tension as Clarissa wonders why she married Richard Dalloway instead of Peter Walsh who was her suitor. The novel also revolves around the story of Septimus Warren Smith, a shell-shocked World War I veteran who suffered from the war and later committed suicide. The novel presents the topic of alienation in many dimensions. Alienation depicts a sense of emotional isolation between individuals or groups within a given community. Indeed, we can sense a feeling of emotional isolation as Peter Walsh feels desperate over his unfulfilling life. This results from the fact that his friends and former suitors have moved on with their lives. Indeed, Peter Walsh was Clarissa’s suitor but Clarissa chose to marry Richard because of his social class. This shows that Peter Walsh suffers from social alienation. As a result, Peter Walsh cries as he regrets losing Clarissa and desperately asks her if she really loves Richard. More so, Peter Walsh’s social alienation manifests where he fails to establish and maintain any stable romantic relationship (Woolf 42-44). Indeed, despite the social world requiring one to make concrete decisions, Peter alienates himself from the social world by lacking the capacity to decide what he feels. As such, he results to talking to himself, which depicts social isolation. On the other hand, we can identify social alienation from the story of Septimus Warren Smith, a shell-shocked World War I veteran. Indeed, Septimus alienated himself from the physical world by constantly residing in the internal world where he talks with his friend Evans who died in the war. He is emotionally numb and encounters deep madness and crazy hallucinations where he sees and hears unreal things that a normal person cannot witness. This is a deviation from the norms and reflects soc ial alienation. In fact, in the social world, people communicate in the real world and not in the internal world as Septimus does. Furthermore, Septimus' presence in the novel is alienation, as he does not relate with any of the other main characters. Ultimately, Septimus suffers the consequences of alienation as he commits suicides after a light moment of joy with his wife. Indeed, Septimus decided in his internal world that he will not go with the doctors to a mental institution and opts to die (Woolf 36-42). This depicts social alienation as people do not commit suicide in the social world but wait for their natural death. More so, we experience Lucrezia’s emotional alienation, as she miserably misses Italy and is tired of taking her husband to various soulless doctors. This depicts social isolation, as the other characters are seemingly comfortable in this place. More so, the novel depicts Clarissa’s alienation from the social world. Indeed, Clarissa’s urge to pay attention to every guest  alienates her from enjoying her evening party. We can see her wishing that she could get some time to talk to Sally and Peter but she is too busy with the other guests. Indeed, Clarissa sought to enjoy her evening party but the events happening during the party hinders her form such enjoyment. This is despite the fact that other people attending the party derive full enjoyment from the party.

Neutral network and machine learning Research Paper

Neutral network and machine learning - Research Paper Example Problems used to be in form of binary strings of 0s and 1s. Currently, there is usage of other encodings. This evolution normally begins from a group of randomly created phenotypes and this process takes place through generations. During each generation, the fitness of each individual in the population/group is cross examined, multiple phenotypes are chosen from the group as per their fitness and then they are modified and can be randomly mutated to create a new population which is then used in the iteration calculations whose procedure is step-by-step also known as the algorithm. This algorithm is mostly terminated after the production of a maximum number of generations. A fulfilling solution may or may not be accomplished if the algorithm has been terminated when because of a maximum number of generations. The most widely accepted representation of the result is using an array of bits. Any other arrays can be used similarly. What makes the representation that uses genetics convenie nt is the fact that their parts can be aligned conveniently because of their fixed size. This facilitates easy crossover operations. 1.2 Applications and results of Genetic Algorithm 1.2.1Metaheuristic This term is designated from a computational method which optimizes problems through iteration. This iteration tries to improve the solution of a candidate as per a given measure of quality. Few or no assumptions are made about the problem being optimized. As far as candidate solutions are involved it can search very large spaces. However, optimal solutions are not guaranteed to be found by Metaheuristic. Stochastic optimization is mostly implemented in a metaheuristic way. It can also be referred to as: Derivative free Direct search Black box Heuristic optimizer 1.2.2 Computational creativity This is also referred to as artificial, mechanical creativity and sometimes creative computation. It comprises of the bringing together of fields such as cognitive psychology, artificial intelli gence and philosophy. Computational creativity improvises the combinational perspective which allows one to model creativity in form of a search procedure through several possible combinations. These combinations can be as a result of composition of different representations. Cross over representations which capture different inputs can be generated using neural networks and genetic algorithms. 1.2.3 Multiple sequence alignment This refers to a sequence alignment of at least 3 biological sequences namely: Protein Dna Rna Most of the times the sequences are assumed to have an evolutionary relationship through which they are descended from a common ancestor hence share a lineage. As a result, sequence homology can be inferred from the Multiple Sequence Alignment and to look into the sequences’ shared evolutionary origins phylogenetic analysis is carried out. In trying to widely simulate the evolutionary process which gave rise to the broadening of the query set, genetic algorit hms have been used for production of Multiple Sequence Alignment.This is done by breaking several potential MSAs into pieces and rearranging the pieces repeatedly.Gaps are introduced at several positions.During simulation a common objective function is achieved which is the sum-of-pairs function that emerges in the broad programming Multiple sequence alignment. 1.3 GA (genetic algorithm) used with NN (neural networks) 1.3.1 Evolving weights The frequent use of GA with NN is because genetic algorithms